Wireless devices and systems including examples of compensating i/q imbalance with neural networks or recurrent neural networks

ABSTRACT

Examples described herein include methods, devices, and systems which may compensate input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN may calculate an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data may also be transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path may be deactivated and the receiver stage may receive an additional RF wireless transmission to be processed in the receiver stage.

BACKGROUND

Digital signal processing for wireless communications, such as digitalbaseband processing or digital front-end implementations, may beimplemented using hardware (e.g. silicon) computing platforms. Forexample, multimedia processing and digital radio frequency (RF)processing may be accomplished by an application-specific integratedcircuit (ASIC) which may implement a digital front-end for a wirelesstransceiver. A variety of hardware platforms are available to implementdigital signal processing, such as the ASIC, a digital signal processor(DSP) implemented as part of a field-programmable gate array (FPGA), ora system-on-chip (SoC). However, each of these solutions often requiresimplementing customized signal processing methods that arehardware-implementation specific. For example, a digital signalprocessor may implement a specific portion of digital processing at acellular base station, such as filtering interference based on theenvironmental parameters at that base station. Each portion of theoverall signal processing performed may be implemented by different,specially-designed hardware, creating complexity.

Moreover, there is an increasing interest in moving wirelesscommunications to “fifth generation” (5G) systems. 5G offers promise ofincreased speed and ubiquity, but methodologies for processing 5Gwireless communications have not yet been set. In some implementationsof 5G wireless communications, “Internet of Things” (IoT) devices mayoperate on a narrowband wireless communication standard, which may bereferred to as Narrow Band IoT (NB-IoT). For example, Release 13 of the3GPP specification describes a narrowband wireless communicationstandard.

Additionally, machine learning (ML) and artificial intelligence (AI)techniques are in need of higher capacity and widely connectedinfrastructures to train devices that use such techniques. For example,machine learning generally refers to a type of AI that may use datasets, often a large volume of data, to train machines on statisticalmethods of analysis. And there is need for higher-capacity memory andmultichip packages to facilitate AI training and inference engines,whether in the cloud or embedded in mobile and edge devices. Forexample, large volumes of data are needed in real time to train AIsystems and accelerate inference. Additionally, there is a need forcommunication capacity at mobile or sensor devices at the edge network,for example, to facilitate processing of data acquired at the edgenetwork, e.g., to efficiently offload such data to a data center for AIor ML techniques to be applied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system arranged in accordancewith examples described herein.

FIG. 2 is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 3 is a schematic illustration of a wireless transmitter.

FIG. 4 is a schematic illustration of wireless receiver.

FIG. 5A is a schematic illustration of an example neural network inaccordance with examples described herein.

FIG. 5B is a schematic illustration of a recurrent neural networkarranged in accordance with examples described herein.

FIGS. 5C-5E are schematic illustrations of example recurrent neuralnetworks in accordance with examples described herein.

FIG. 6 is a schematic illustration of a time frame for a time-divisionmultiplexing time period arranged in accordance with examples describedherein.

FIG. 7A is a schematic illustration of an I/Q imbalance compensationmethod in accordance with examples described herein.

FIG. 7B is a flowchart of a method in accordance with examples describedherein.

FIG. 8 is a block diagram of a computing device arranged in accordancewith examples described herein.

FIG. 9 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 10 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 11 is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 12 is a schematic illustration of an electronic device arranged inaccordance with examples described herein.

FIG. 13 is a schematic illustration of a wireless communications systemarranged in accordance with aspects of the present disclosure.

FIG. 14 is a schematic illustration of a communications system arrangedin accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

Examples described herein include wireless devices and systems which mayinclude examples of reducing in-band interference signals andcompensating power amplifier noise. Embodiments of compensating poweramplifier noise have been described, for example, in U.S. applicationSer. No. 16/849,696, which application is incorporated herein byreference in its entirety for any purpose. Digital pre-mismatch (DPM)and digital pre-distortion (DPD) filters may be utilized to compensate,respectively, in-phase branch/quadrature branch (I/Q) imbalance andnonlinear power amplifier noise. For example, transmission signals areprovided on the I branch and the Q branch and may undergo imbalance ormismatch. Such I/Q imbalance may be difficult to model, and,accordingly, DPM filters are utilized to compensate such I/Q imbalance.I/Q imbalance may also be referred to as I/Q mismatch. In addition tocompensating the in-phase branch/quadrature branch (I/Q) imbalance ornoise related thereto, a nonlinear power amplifier noise may becompensated by the DPD filters, such as the power amplifier noise foundin wireless devices and systems with power amplifiers. For example, anRF power amplifier (PA) may be utilized in transmitters of wirelessdevices and systems to amplify wireless transmission signals that are tobe transmitted. Such nonlinear power amplifier noise from poweramplifiers may be difficult to model, and, accordingly, DPD filters areutilized to compensate such nonlinear power amplifier noise, therebyreducing noises introduced into the wireless transmission signal from apower amplifier during transmission. Conventional wireless devices andsystems may utilize specially-designed hardware to implement a DPMfilter and a DPD filter in a wireless device or system. For example, aDPM filter and a DPD filter may be implemented in a variety of hardwareplatforms, as part of a wireless transceiver or transmitter.

As described herein, a recurrent neural network (RNN) in a wirelessdevice or system may utilize feedback after processing of a compensatedwireless transmission signal to determine how efficiently the DPM andDPD filter are compensating such wireless transmission signals. Forexample, in determining how efficiently the DPM filter and the DPDfilter are performing compensation for, respectively, I/Q imbalance andnonlinear power amplifier noise, the recurrent neural network maycalculate an error signal. The calculated error signal may be between aninitial wireless transmission signal and the compensated I/Q imbalancesignal to reduce error in a model of the DPM filter (e.g., coefficientdata utilized to model a compensation filter). The calculated errorsignal may be between an initial wireless transmission signal and thecompensated, amplified wireless transmission signal to reduce error in amodel of the DPD filter (e.g., filter coefficient data utilized to modela compensation filter). Conventional wireless devices may include aspecific path with a receiver portion to process a feedback signal at aDPM filter and a DPD filter, which may be inefficient in utilizingcomputational resources and/or board space to provide such a path forthe feedback. That specific path with the receiver portion to processthe feedback signal may be in addition to a wireless receiver path for awireless receiver portion of the wireless device. Accordingly, chiparchitectures in which the feedback signal is provided to a recurrentneural network in an efficient scheme may be desired to reducecomputational resources needed and/or optimize the board space of thatwireless chip.

In the examples described herein, a time division duplexing (TDD)configured radio frame is utilized in conjunction with a single receiverpath to provide both a feedback signal to a recurrent neural network andto receive wireless transmission signals, which may be received at awireless receiver portion of a wireless device. In accordance with theexamples described herein, a switch may activate a path to provide thefeedback signal through the wireless receiver path to the recurrentneural network, when the wireless receiver path is not receiving anactive wireless signal. For example, the wireless receiver path may notreceive an active wireless signal during an uplink time period of a TDDconfigured radio frame. The uplink time period of the TDD configuredradio frame can be referred to as an uplink transmission time interval(TTI). Similarly, the downlink time period of the TDD configured radioframe can be referred to as a downlink transmission time interval (TTI).During an uplink TTI, the switch may be activated to provide thefeedback through the wireless receiver path to the recurrent neuralnetwork. In providing the feedback over multiple uplink TTIs, therecurrent neural network may provide respective filter coefficient dataof respective models for the DPM or DPD filter, which compensates for,respectively, I/Q imbalance and nonlinear power amplifier noise. Forexample, radio frequency (RF) signals to be transmitted may be processedby the DPM filter in accordance with respective filter coefficient data,prior to an input of those signals to D/A converter or a RF mixer.Advantageously, in using the provided filter coefficient data for theDPM filter, the DPM filter may compensate for I/Q imbalance in RFsignals, prior to processing of the RF signals at a digital-to-analog(D/A) converter or mixer.

Additionally, during downlink TTIs, the switch may deactivate the paththat provides feedback through the wireless receiver path, so that thewireless receiver portion of a wireless transceiver may receive wirelesstransmission signals, thereby providing for efficient TDD frames to bothprovide the feedback signal to the recurrent neural network and toreceive wireless signals using the same wireless receiver path.

Examples described herein additionally include systems and methods whichinclude wireless devices and systems with examples of mixing input datawith pluralities of coefficients in multiple layers ofmultiplication/accumulation units (MAC units) and corresponding memorylook-up units (MLUs). For example, a number of layers of MAC units maycorrespond to a number of wireless channels, such as a number ofchannels received at respective antennas of a plurality of antennas. Inaddition, a number of MAC units and MLUs utilized is associated with thenumber of channels. For example, a second layer of MAC units and MLUsmay include a number, such as “m-1”, MAC units and MLUs, where “m”represents the number of antennas, each antenna receiving a portion ofinput data. Advantageously, in utilizing such a hardware framework, theprocessing capability of generated output data may be maintained whilereducing a number of MAC units and MLUs, which are utilized for suchprocessing in an electronic device. In some examples, however, whereboard space may be available, a hardware framework may be utilized thatincludes “m” MAC units and “m” MLUs in each layer, where m representsthe number of antennas.

Multi-layer neural networks (NNs) and/or multi-layer recurrent neuralnetworks (RNNs) may be used to determine and provide filter coefficientdata. Accordingly, a wireless device or system that implements a NN orRNN, as described herein, may compensate for such (I/Q) imbalance ornonlinear power amplifier noise. The NNs and/or RNNs may have nonlinearmapping and distributed processing capabilities which may beadvantageous in many wireless systems, such as those involved inprocessing wireless input data having time-varying wireless channels(e.g., autonomous vehicular networks, drone networks, orInternet-of-Things (IoT) networks).

FIG. 1 is a schematic illustration of a system 100 arranged inaccordance with examples described herein. System 100 includeselectronic device 102, electronic device 110, antenna 101, antenna 103,antenna 105, antenna 107, antenna 121, antenna 123, antenna 125, antenna127, wireless transmitter 111, wireless transmitter 113, wirelessreceiver 115, wireless receiver 117, wireless transmitter 131, wirelesstransmitter 133, wireless receiver 135 and, wireless receiver 137. Theelectronic device 102 may include antenna 121, antenna 123, antenna 125,antenna 127, wireless transmitter 131, wireless transmitter 133,wireless receiver 135, and wireless receiver 137. The electronic device110 may include antenna 101, antenna 103, antenna 105, antenna 107,wireless transmitter 111, wireless transmitter 113, wireless receiver115, and wireless receiver 117. In operation, electronic devices 102,110 can communicate wireless communication signals between therespective antennas of each electronic device. In an example of a TDDmode, wireless transmitter 131 coupled to antenna 121 may transmit toantenna 105 coupled to wireless receiver 115 during an uplink period ofthe TDD configured radio frame, while, at the same time or during atleast a portion of the same time, the wireless transmitter may alsoactivate a switch path that provides a feedback signal to a recurrentneural network of wireless transmitter 131.

The recurrent neural network of wireless transmitter 131 may provide thefilter coefficient data that are utilized in a model to at leastpartially compensate for power amplifier noise internal to the wirelesstransmitter 131. The wireless transmitter 131 may include a poweramplifier that amplifies wireless transmission signals before providingsuch respective wireless transmission signals to the antenna 121 for RFtransmission. In some examples, the recurrent neural network of thewireless transmitter 131 may also provide (e.g., optimize) filtercoefficient data to also at least partially compensate power amplifiernoise from other components of the electronic device 102, such as apower amplifier of the wireless transmitter 133. After an uplink periodof a time division duplexing (TDD) configured radio frame has passed,the wireless receiver 135 and/or the wireless receiver 137 may receivewireless signals during a downlink period of the time division duplexingconfigured radio frame. For example, the wireless receiver 135 and/orthe wireless receiver 137 may receive individual signals or acombination of signals (e.g., a MIMO signal) from the electronic device110, having transmitted wireless signals from the wireless transmitter111 coupled to the antenna 101 and/or from the wireless transmitter 113coupled to the antenna 103. Power amplifier noise may generally refer toany noise in a signal to be transmitted from an electronic device thatmay be at least partially generated by one or more power amplifiers ofthat electronic device.

Electronic devices described herein, such as electronic device 102 andelectronic device 110 shown in FIG. 1 may be implemented using generallyany electronic device for which communication capability is desired. Forexample, electronic device 102 and/or electronic device 110 may beimplemented using a mobile phone, smartwatch, computer (e.g. server,laptop, tablet, desktop), or radio. In some examples, the electronicdevice 102 and/or electronic device 110 may be incorporated into and/orin communication with other apparatuses for which communicationcapability is desired, such as but not limited to, a wearable device, amedical device, an automobile, airplane, helicopter, appliance, tag,camera, or other device.

While not explicitly shown in FIG. 1, electronic device 102 and/orelectronic device 110 may include any of a variety of components in someexamples, including, but not limited to, memory, input/output devices,circuitry, processing units (e.g. processing elements and/orprocessors), or combinations thereof.

The electronic device 102 and the electronic device 110 may each includemultiple antennas. For example, the electronic device 102 and electronicdevice 110 may each have more than two antennas. Three antennas each areshown in FIG. 1, but generally any number of antennas may be usedincluding 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, or 64antennas. Other numbers of antennas may be used in other examples. Insome examples, the electronic device 102 and electronic device 110 mayhave a same number of antennas, as shown in FIG. 1. In other examples,the electronic device 102 and electronic device 110 may have differentnumbers of antennas. Generally, systems described herein may includemultiple-input, multiple-output (“MIMO”) systems. MIMO systems generallyrefer to systems including one or more electronic devices which transmittransmissions using multiple antennas and one or more electronic deviceswhich receive transmissions using multiple antennas. In some examples,electronic devices may both transmit and receive transmissions usingmultiple antennas. Some example systems described herein may be “massiveMIMO” systems. Generally, massive MIMO systems refer to systemsemploying greater than a certain number (e.g. 64) antennas to transmitand/or receive transmissions. As the number of antennas increase, so togenerally does the complexity involved in accurately transmitting and/orreceiving transmissions.

Although two electronic devices (e.g. electronic device 102 andelectronic device 110) are shown in FIG. 1, generally the system 100 mayinclude any number of electronic devices.

Electronic devices described herein may include receivers, transmitters,and/or transceivers. For example, the electronic device 102 of FIG. 1includes wireless transmitter 131 and wireless receiver 135, and theelectronic device 110 includes wireless transmitter 111 and wirelessreceiver 115. Generally, receivers may be provided for receivingtransmissions from one or more connected antennas, transmitters may beprovided for transmitting transmissions from one or more connectedantennas, and transceivers may be provided for receiving andtransmitting transmissions from one or more connected antennas. Whileboth electronic devices 102, 110 are depicted in FIG. 1 with individualwireless transmitter and individual wireless receivers, it can beappreciated that a wireless transceiver may be coupled to antennas ofthe electronic device and operate as either a wireless transmitter orwireless receiver, to receive and transmit transmissions. For example, atransceiver of electronic device 102 may be used to providetransmissions to and/or receive transmissions from antenna 121, whileother transceivers of electronic device 110 may be provided to providetransmissions to and/or receive transmissions from antenna 101 andantenna 103. Generally, multiple receivers, transmitters, and/ortransceivers may be provided in an electronic device—one incommunication with each of the antennas of the electronic device. Thetransmissions may be in accordance with any of a variety of protocols,including, but not limited to 5G signals, and/or a variety ofmodulation/demodulation schemes may be used, including, but not limitedto: orthogonal frequency division multiplexing (OFDM), filter bankmulti-carrier (FBMC), the generalized frequency division multiplexing(GFDM), universal filtered multi-carrier (UFMC) transmission, biorthogonal frequency division multiplexing (BFDM), sparse code multipleaccess (SCMA), non-orthogonal multiple access (NOMA), multi-user sharedaccess (MUSA) and faster-than-Nyquist (FTN) signaling withtime-frequency packing. In some examples, the transmissions may be sent,received, or both, in accordance with 5G protocols and/or standards.

Examples of transmitters, receivers, and/or transceivers describedherein, such as the wireless transmitter 131 and the wirelesstransmitter 111 may be implemented using a variety of components,including, hardware, software, firmware, or combinations thereof. Forexample, transceivers, transmitters, or receivers may include circuitryand/or one or more processing units (e.g. processors) and memory encodedwith executable instructions for causing the transceiver to perform oneor more functions described herein (e.g. software).

FIG. 2 is a schematic illustration of an electronic device 200 arrangedin accordance with examples described herein. Electronic device 200includes a baseband transmitter (Tx) 215 and a baseband receiver (Rx)285, each respectively having a transmitter path and a receiver pathto/from transmitting antenna (Tx) 250 and receiving antenna (Rx) 255.Electronic device 200 may represent an implementation of the electronicdevice 102, 110; with the baseband transmitter 215 and transmitter pathrepresenting a wireless transmitter 131, 133 or wireless transmitter111, 113: and with the baseband receiver 285 representing a wirelessreceiver 135, 137 or wireless receiver 115, 117.

After having received a signal to be transmitted t(n) 210, the basebandtransmitter 215 may perform baseband processing on that signal to betransmitted t(n) 210 to generate a baseband signal to be transmittedt(n) 216. The signal 216 is provided to the recurrent neural network 280and also provided, along the transmitter path towards the transmittingantenna 250, to a digital pre-distortion (DPD) filter 220. For example,the electronic device 200 may include a transmitter path from the DPDfilter 220 towards baseband transmitting antenna 250 along which thesignal 216 is processed that is a first communication path. The DPDfilter 220 at least partially compensates the signal t(n) 216 based on amodel including filter coefficient data provided to the DPD filter bythe recurrent neural network 280. The DPD filter 220 utilizes the modelbased on the filter coefficient data to at least partially compensatethe signal 216 for noise in the electronic device 200, such as nonlinearpower amplifier noise generated by the power amplifier 240. As will bedescribed with respect to the recurrent neural network 280, the filtercoefficient data may be determined to reduce the error introduced intothe signal to be transmitted t(n) 216 by nonlinear power amplifiernoise, when that signal 216 is amplified by power amplifier 240 fortransmission at the transmitting antenna 250. The DPD filter 220utilizes the provided filter coefficient data to wholly and/or partiallycompensate for nonlinear power amplifier noise introduced by the poweramplifier 240, e.g., prior to input of additional input signals to beprovided to the power amplifier 240. While referred to as the DPD filter220, it can be appreciated that various digital filters may implementthe DPD filter 220, and thus the DPD filter 220 may also be referred toas a digital filter.

After having been at least partially compensated for noise by the DPDfilter 220, the signal to be transmitted t(n) may be further processedalong the transmitter path towards the transmitting antenna 250.Accordingly, the compensated signal 216 is processed by the numericallycontrolled oscillator (NCO) 225, the digital to analog converter (DAC)230, the intermediate frequency (IF) filter 235, the mixer 237 inconjunction with a provided local oscillating signal from the localoscillator 290, and the power amplifier 240 to generate amplified signalto be transmitted T(n) 247. The signal to be transmitted T(n) 247 isprovided to the transmitting antenna 250 via a switch 245. Thetransmitter path to the transmitting antenna 250 includes a path throughthe switch 245 for transmission of any signal to be transmitted. Thatsame amplified signal to be transmitted T(n) 247 is provided to thereceiver path via the switch 245, when the switch 245 is activated, asthe signal X(n) 249. The electronic device 200 may include the receiverpath from the switch 245 towards baseband receiver 285 along which thesignal X(n) 249 is processed that may be referred to as a secondcommunication path.

The switch 245 may be activated by a control signal (e.g., a selectionsignal) that indicates an uplink (TTI) is occurring in a time divisionduplexing configured radio frame that the electronic device 200utilizes. When the switch 245 is activated, the amplified signal to betransmitted T(n) 247 is provided to the receiver path of the electronicdevice 200 to be used as a feedback signal in calculations performed bythe recurrent neural network 280. The amplified signal to be transmittedT(n) 247 is provided to the receiver path as the signal X(n) 249,starting at the low noise amplifier (LNA) 260. The signal X(n) 249 andthe amplified signal to be transmitted T(n) 247 represent the samesignal processed by the power amplifier 240. The signal X(n) 249 and theamplified signal to be transmitted T(n) 247 are both provided by theswitch 245, when the switch 245 is activated, to the receiver path ofthe electronic device 200 and the transmitting antenna 250,respectively. Accordingly, the signal X(n) 249 is processed by the LNA260, the mixer 263 in conjunction with the provided local oscillatingsignal from the local oscillator 290, the intermediate frequency (IF)filter 265, the analog-to-digital converter 270, and the numericallycontrolled oscillator (NCO) 275 to generate the feedback signal X(n) 277that is provided to the recurrent neural network 280. The recurrentneural network 280 may also receive the control signal indicating thatan uplink time period is occurring, and may receive the feedback signalX(n) 277 to process that signal in a calculation to reduce the errorintroduced by the nonlinear power amplifier noise generated by the poweramplifier 240.

After receiving the feedback signal X(n) 277, the recurrent neuralnetwork 280 may determine to calculate an error signal between thesignal to be transmitted t(n) 216 and the compensated wirelesstransition signal to reduce error in a model of the DPD filter 220. Therecurrent neural network utilizes the error signal to determine and/orupdate filter coefficient data B(n) 243 provided to the DPD filter 220for utilization in a model of the DPD filter 220 that at least partiallycompensates nonlinear power amplifier noise. For the recurrent neuralnetwork 280 to calculate the filter coefficient data to be utilized inthe DPD filter 220, the recurrent neural network 280 may compute anerror signal for reducing a difference between the signal to betransmitted t(n) 216 that is input to the DPD filter 220 and thefeedback signal X(n) 277. For example, the difference may be reduced(e.g., minimized) by utilizing Equation (1):

$\begin{matrix}{{z(k)} = {\left\lbrack {1 + {\sum\limits_{p = 2}^{P}{\sum\limits_{m = {- M}}^{M}{a_{p,m} \cdot {{y\left( {k - m} \right)}}^{p - 1}}}}} \right\rbrack \cdot {y(k)}}} & (1)\end{matrix}$

The signal to be transmitted t(n) 216 may be calculated in Equation (1)as z(k). The feedback signal X(n) 277 may be calculated in Equation (1)as y(k), to be summed over ‘p’ and ‘m,’ where ‘P’ represents thenonlinear order of the power amplifier noise to be compensated and ‘M’represents a “memory” of the recurrent neural network 280. For example,the recurrent neural network may store previous versions of the feedbacksignal X(n) 277, with the ‘m’ term representative of an offset of thefeedback signal X(n) 277, such that the offset indicates a number oftime periods between a received feedback signal X(n) 277 and a previousversion of the feedback signal X(n) 277, received at ‘m’ time periodsbefore the feedback signal X(n) 277 had been received at the recurrentneural network 280 to perform the calculation. In the example, ‘P’ mayrepresent the number of filter taps for a model of the DPD filter 220 toat least partially compensate a nonlinearity of the power amplifiernoise. In various implementations, ‘P’ may equal 1, 2, 3, 4, 7, 9, 10,12, 16, 20, 100, or 200. Additionally or alternatively, ‘M’ may equal 0,1, 2, 3, 4, 7, 9, 10, 12, 16, 20, 100, or 200. The recurrent neuralnetwork 280 may utilize Equation (1) in conjunction with an algorithm toreduce (e.g., minimize) the difference between z(k) and y(k), such asleast-mean-squares (LMS) algorithm, least-squares (LS) algorithm, ortotal-least-squares (TLS) algorithm. Accordingly, in reducing thedifference between z(k) and y(k), the recurrent neural networkdetermines the filter coefficient data B(n) 243, as the terms a_(p,m) inEquation (1), to be utilized in the DPD filter 220. In someimplementations, sample vectors may be utilized, instead of the signalto be transmitted t(n) 216, to determine an initial set of the filtercoefficient data B(n) 243.

In some examples, the recurrent neural network determines and providesthe filter coefficient data B(n) 243 to be utilized in the DPD filter220 as a “memoryless” system in which the filter coefficient data B(n)243 updates the DPD filter 220 with new filter coefficient data,replacing any filter coefficient data that the DPD filter utilizedbefore receiving the filter coefficient data B(n) 243. Updating the DPDfilter 220 with the filter coefficient data B(n) 243 may be referred toas optimizing the filter coefficient data, with some or all of thefilter coefficient data being updated. For example, Equation (1) may bereduced to Equation (2) when other versions of the feedback signal X(n)277 are not utilized in the calculation, thereby reduced the ‘m’ term tozero, such that Equation (1) reduces to Equation (2):

$\begin{matrix}{{z(k)} = {\left\lbrack {1 + {\sum\limits_{p = 2}^{P}{a_{\rho} \cdot {{y(k)}}^{p - 1}}}} \right\rbrack \cdot {y(k)}}} & (2)\end{matrix}$

In utilizing the same receiver path for processing of a received signaland the aforementioned generation of a feedback signal, the electronicdevice 200 may utilize board space and/or resources on a circuitimplementing the electronic device 200, as compared to an electronicdevice that includes a separate path for the feedback signal and aseparate path for processing of a received signal. For example,electronic device 200 utilizes the LNA 260, the mixer 263 in conjunctionwith the provided local oscillating signal from the local oscillator290, the intermediate frequency (IF) filter 265, the analog-to-digitalconverter 270, and the numerically controlled oscillator (NCO) 275 forboth generation of a feedback signal X(n) 277 and for processing of areceived signal R(n) 257. As described, when the switch 245 isactivated, the electronic device 200 utilizes the LNA 260, the mixer 263in conjunction with the provided local oscillating signal from the localoscillator 290, the intermediate frequency (IF) filter 265, theanalog-to-digital converter 270, and the numerically controlledoscillator (NCO) 275 to generate a feedback signal X(n) 277 andcalculates filter coefficient data with the recurrent neural network280. When the switch 245 is deactivated, the electronic device 200utilizes the LNA 260, the mixer 263 in conjunction with the providedlocal oscillating signal from the local oscillator 290, the intermediatefrequency (IF) filter 265, the analog-to-digital converter 270, and thenumerically controlled oscillator (NCO) 275 to receive and process thereceived signal R(n) 257.

The switch 245 may be deactivated at the end of activation period. Forexample, the control signal that activates the switch 245 may includeinformation that specifies how long the switch 245 is to be activated,e.g., an activation period. The activation period may be the same as anuplink TTI of a time-division duplexing configured radio frame that theelectronic device 200 utilizes. For example, as described with referenceto FIG. 6, the activation period may be a specific uplink TTI thatoperates at a different time period than a downlink TTI. In someexamples, the switch 245 may be activated for the length of the signal216, which may be the same length as the signal 210. Additionally oralternatively, the switch 245 may be deactivated when a wireless signalis detected at the receiving antenna 255. For example, a control signalmay indicate the start of a downlink TTI when a signal is detected atthe receiving antenna 255, which indicates that the activation periodhas finished. Accordingly, the switch 245 may deactivated.

The switch 245 may be deactivated by a control signal that indicates adownlink TTI is occurring in a time division duplexing configured radioframe that the electronic device 200 utilizes. Accordingly, a signalX(n) 249 is not provided to the receiver path of the electronic device200 because the switch 245 is deactivated. With the switch 245deactivated, the received signal R(n) 257 is provided to the receiverpath of the electronic device 200 to processed in the receiver path forthe generation of a baseband received signal r(n) 287. The receivedsignal R(n) 257 is provided to the receiver path, starting at the lownoise amplifier (LNA) 260. Accordingly, the received signal R(n) 257 isprocessed by the LNA 260, the mixer 263 in conjunction with the providedlocal oscillating signal from the local oscillator 290, the intermediatefrequency (IF) filter 265, the analog-to-digital converter 270, thenumerically controlled oscillator (NCO) 275, and the baseband receiver285 to generate the baseband received signal 287. In generating thebaseband received signal 287, the electronic device 200 utilizes thesame receiver path that is utilized to generate and provide a feedbacksignal to the recurrent neural network 280, thereby efficientlyutilizing the computational resources and/or board space of theelectronic device 200. Accordingly, the same receiver path of electronicdevice 200 is utilized for the receiving wireless signals duringdownlink time periods and providing feedback signals to the recurrentneural network during uplink time periods. In some examples, therecurrent neural network 280, while not being provided a feedback signalX(n) 277 during the downlink time period, may calculate and/or determinefilter coefficient data while the received signal R(n) 257 is beingprocessed. Accordingly, in conjunction with time division duplexingconfigured radio frames, the electronic device 200 utilizes a singlereceiver path to provide both the feedback signal X(n) 277 to therecurrent neural network 280 and to receive wireless transmissionsignals, such as the received signal R(n) 257 to provide basebandreceived signals r(n) 287.

FIG. 3 is a schematic illustration of a wireless transmitter 300. Thewireless transmitter 300 receives a data signal 311 and performsoperations to generate wireless communication signals for transmissionvia the antenna 303. The transmitter output data x_(N)(n) 310 isamplified by a power amplifier 332 before the output data aretransmitted on an RF antenna 303. The operations to the RF-front end maygenerally be performed with analog circuitry or processed as a digitalbaseband operation for implementation of a digital front-end. Theoperations of the RF-front end include a scrambler 304, a coder 308, aninterleaver 312, a modulation mapping 316, a frame adaptation 320, anIFFT 324, a guard interval 328, and frequency up-conversion 330.

The scrambler 304 may convert the input data to a pseudo-random orrandom binary sequence. For example, the input data may be a transportlayer source (such as MPEG-2 Transport stream and other data) that isconverted to a Pseudo Random Binary Sequence (PRBS) with a generatorpolynomial. While described in the example of a generator polynomial,various scramblers 304 are possible.

The coder 308 may encode the data outputted from the scrambler to codethe data. For example, a Reed-Solomon (RS) encoder, turbo encoder may beused as a first coder to generate a parity block for each randomizedtransport packet fed by the scrambler 304. In some examples, the lengthof parity block and the transport packet can vary according to variouswireless protocols. The interleaver 312 may interleave the parity blocksoutput by the coder 308, for example, the interleaver 312 may utilizeconvolutional byte interleaving. In some examples, additional coding andinterleaving can be performed after the coder 308 and interleaver 312.For example, additional coding may include a second coder that mayfurther code data output from the interleaver, for example, with apunctured convolutional coding having a certain constraint length.Additional interleaving may include an inner interleaver that formsgroups of joined blocks. While described in the context of a RS coding,turbo coding, and punctured convolution coding, various coders 308 arepossible, such as a low-density parity-check (LDPC) coder or a polarcoder. While described in the context of convolutional byteinterleaving, various interleavers 312 are possible.

The modulation mapping 316 may modulate the data output from theinterleaver 312. For example, quadrature amplitude modulation (QAM) maybe used to map the data by changing (e.g., modulating) the amplitude ofthe related carriers. Various modulation mappings may be used,including, but not limited to: Quadrature Phase Shift Keying(QPSK), SCMANOMA, and MUSA (Multi-user Shared Access). Output from the modulationmapping 316 may be referred to as data symbols. While described in thecontext of QAM modulation, various modulation mappings 316 are possible.The frame adaptation 320 may arrange the output from the modulationmapping according to bit sequences that represent correspondingmodulation symbols, carriers, and frames.

The IFFT 324 may transform symbols that have been framed intosub-carriers (e.g., by frame adaptation 320) into time-domain symbols.Taking an example of a 5G wireless protocol scheme, the IFFT can beapplied as N-point IFFT:

$\begin{matrix}{x_{k} = {\overset{N}{\sum\limits_{n = 1}}{X_{n}e^{i\; 2\;\pi\;{{kn}/N}}}}} & (3)\end{matrix}$

where X_(n) is the modulated symbol sent in the nth 5G sub-carrier.Accordingly, the output of the IFFT 324 may form time-domain 5G symbols.In some examples, the IFFT 324 may be replaced by a pulse shaping filteror poly-phase filtering banks to output symbols for frequencyup-conversion 330.

In the example of FIG. 3, the guard interval 328 adds a guard intervalto the time-domain 5G symbols. For example, the guard interval may be afractional length of a symbol duration that is added, to reduceinter-symbol interference, by repeating a portion of the end of atime-domain 5G symbol at the beginning of the frame. For example, theguard interval can be a time period corresponding to the cyclic prefixportion of the 5G wireless protocol scheme.

The frequency up-conversion 330 may up-convert the time-domain 5Gsymbols to a specific radio frequency. For example, the time-domain 5Gsymbols can be viewed as a baseband frequency range and a localoscillator can mix the frequency at which it oscillates with the 5Gsymbols to generate 5G symbols at the oscillation frequency. A digitalup-converter (DUC) may also be utilized to convert the time-domain 5Gsymbols. Accordingly, the 5G symbols can be up-converted to a specificradio frequency for an RF transmission.

Before transmission, at the antenna 303, a power amplifier 332 mayamplify the transmitter output data x_(N)(n) 310 to output data for anRF transmission in an RF domain at the antenna 303. The antenna 303 maybe an antenna designed to radiate at a specific radio frequency. Forexample, the antenna 303 may radiate at the frequency at which the 5Gsymbols were up-converted. Accordingly, the wireless transmitter 300 maytransmit an RF transmission via the antenna 303 based on the data signal311 received at the scrambler 304. As described above with respect toFIG. 3, the operations of the wireless transmitter 300 can include avariety of processing operations. Such operations can be implemented ina conventional wireless transmitter, with each operation implemented byspecifically-designed hardware for that respective operation. Forexample, a DSP processing unit may be specifically-designed to implementthe IFFT 324. As can be appreciated, additional operations of wirelesstransmitter 300 may be included in a conventional wireless receiver.

The wireless transmitter 300 may be utilized to implement the wirelesstransmitters 111, 113 or wireless transmitters 131, 133 of FIG. 1, forexample. The wireless transmitter 300 may also represent a configurationin which the DPM filter 1118 and the recurrent neural network 1180 orrecurrent neural network 1294 may be utilized. For example, a DPM filter1118 may at least partially compensate the data signal 311 beforeproviding the data signal 311 to the scrambler 304. A recurrent neuralnetwork 1180 may be implemented in the wireless transmitter 300, withsignal paths to the recurrent neural network from any element of thetransmitter path of the wireless transmitter 300.

FIG. 4 is a schematic illustration of wireless receiver 400. Thewireless receiver 400 receives input data X (i,j) 410 from an antenna405 and performs operations of a wireless receiver to generate receiveroutput data at the descrambler 444. The antenna 405 may be an antennadesigned to receive at a specific radio frequency. The operations of thewireless receiver may be performed with analog circuitry or processed asa digital baseband operation for implementation of a digital front-end.The operations of the wireless receiver include a frequencydown-conversion 412, guard interval removal 416, a fast Fouriertransform 420, synchronization 424, channel estimation 428, ademodulation mapping 432, a deinterleaver 436, a decoder 440, and adescrambler 444.

The frequency down-conversion 412 may down-convert the frequency domainsymbols to a baseband processing range. For example, continuing in theexample of a 5G implementation, the frequency-domain 5G symbols may bemixed with a local oscillator frequency to generate 5G symbols at abaseband frequency range. A digital down-converter (DDC) may also beutilized to convert the frequency domain symbols. Accordingly, the RFtransmission including time-domain 5G symbols may be down-converted tobaseband. The guard interval removal 416 may remove a guard intervalfrom the frequency-domain 5G symbols. The FFT 420 may transform thetime-domain 5G symbols into frequency-domain 5G symbols. Taking anexample of a 5G wireless protocol scheme, the FFT can be applied asN-point FFT:

$\begin{matrix}{X_{n} = {\sum\limits_{k = 1}^{N}{x_{k}e^{{- i}\; 2\pi\;{{kn}/N}}}}} & (4)\end{matrix}$

where X_(n) is the modulated symbol sent in the nth 5G sub-carrier.Accordingly, the output of the FFT 420 may form frequency-domain 5Gsymbols. In some examples, the FFT 420 may be replaced by poly-phasefiltering banks to output symbols for synchronization 424.

The synchronization 424 may detect pilot symbols in the 5G symbols tosynchronize the transmitted data. In some examples of a 5Gimplementation, pilot symbols may be detected at the beginning of aframe (e.g., in a header) in the time-domain. Such symbols can be usedby the wireless receiver 400 for frame synchronization. With the framessynchronized, the 5G symbols proceed to channel estimation 428. Thechannel estimation 428 may also use the time-domain pilot symbols andadditional frequency-domain pilot symbols to estimate the time orfrequency effects (e.g., path loss) to the received signal.

For example, a channel may be estimated according to N signals receivedthrough N antennas (in addition to the antenna 405) in a preamble periodof each signal. In some examples, the channel estimation 428 may alsouse the guard interval that was removed at the guard interval removal416. With the channel estimate processing, the channel estimation 428may at least partially compensate for the frequency-domain 5G symbols bysome factor to reduce the effects of the estimated channel. Whilechannel estimation has been described in terms of time-domain pilotsymbols and frequency-domain pilot symbols, other channel estimationtechniques or systems are possible, such as a MIMO-based channelestimation system or a frequency-domain equalization system.

The demodulation mapping 432 may demodulate the data outputted from thechannel estimation 428. For example, a quadrature amplitude modulation(QAM) demodulator can map the data by changing (e.g., modulating) theamplitude of the related carriers. Any modulation mapping describedherein can have a corresponding demodulation mapping as performed bydemodulation mapping 432. In some examples, the demodulation mapping 432may detect the phase of the carrier signal to facilitate thedemodulation of the 5G symbols. The demodulation mapping 432 maygenerate bit data from the 5G symbols to be further processed by thedeinterleaver 436.

The deinterleaver 436 may deinterleave the data bits, arranged as parityblock from demodulation mapping into a bit stream for the decoder 440,for example, the deinterleaver 436 may perform an inverse operation toconvolutional byte interleaving. The deinterleaver 436 may also use thechannel estimation to at least partially compensate for channel effectsto the parity blocks.

The decoder 440 may decode the data outputted from the scrambler to codethe data. For example, a Reed-Solomon (RS) decoder or turbo decoder maybe used as a decoder to generate a decoded bit stream for thedescrambler 444. For example, a turbo decoder may implement a parallelconcatenated decoding scheme. In some examples, additional decodingand/or deinterleaving may be performed after the decoder 440 anddeinterleaver 436. For example, additional decoding may include anotherdecoder that may further decode data output from the decoder 440. Whiledescribed in the context of a RS decoding and turbo decoding, variousdecoders 440 are possible, such as low-density parity-check (LDPC)decoder or a polar decoder.

The descrambler 444 may convert the output data from decoder 440 from apseudo-random or random binary sequence to original source data. Forexample, the descrambler 44 may convert decoded data to a transportlayer destination (e.g., MPEG-2 transport stream) that is descrambledwith an inverse to the generator polynomial of the scrambler 304. Thedescrambler thus outputs receiver output data. Accordingly, the wirelessreceiver 400 receives an RF transmission including input data X (i,j)410 via to generate the receiver output data.

As described herein, for example with respect to FIG. 4, the operationsof the wireless receiver 400 can include a variety of processingoperations. Such operations can be implemented in a conventionalwireless receiver, with each operation implemented byspecifically-designed hardware for that respective operation. Forexample, a DSP processing unit may be specifically-designed to implementthe FFT 420. As can be appreciated, additional operations of wirelessreceiver 400 may be included in a conventional wireless receiver.

The wireless receiver 400 may be utilized to implement the wirelessreceivers the wireless receivers 115, 117 or wireless receivers 135, 137of FIG. 1, for example. The wireless receiver 400 may also represent aconfiguration in which the recurrent neural network 1180 or recurrentneural network 1294 may be utilized. For example, wireless receiver 400may provide a feedback signal to a recurrent neural network 1294 afterdescrambling the feedback signal at the descrambler 444. Accordingly, arecurrent neural network 1294 may be implemented in the wirelessreceiver 400 with a signal path to the recurrent neural network from thereceiver path of the wireless receiver 400.

FIG. 5A is a schematic illustration of an example neural network 500arranged in accordance with examples described herein. The neuralnetwork 500 includes a network of processing elements 504, 506, 509 thatoutput adjusted signals y₁(n), y₂(n), y₃(n), y_(L)(n) 508 based on thefeedback signal X(n) 277, depicted in FIG. 5A as x₁(n), x₂(n), x₃(n),x_(N)(n) 502. The processing elements 504 receive the feedback signalX(n) 277 as x₁(n), x₂(n), x₃(n), x_(N)(n) 502 as inputs.

The processing elements 504 may be implemented, for example, using bitmanipulation units that may forward the signals x₁(n), x₂(n), x₃(n),x_(N)(n) 502 to processing elements 506. In some implementations, a bitmanipulation unit may perform a digital logic operation on a bitwisebasis. For example, a bit manipulation unit may be a NOT logic unit, anAND logic unit, an OR logic unit, a NOR logic unit, a NAND logic unit,or an XOR logic unit. Processing elements 506 may be implemented, forexample, using multiplication units that include a nonlinear vector set(e.g., center vectors) based on a nonlinear function, such as a Gaussianfunction (e.g., :

${{f(r)} = {\exp\;\left( {- \frac{r^{2}}{a^{2}}} \right)}},$

a multi-quadratic function (e.g., ƒ(r)=(r²+σ²)), an inversemulti-quadratic function (e.g., ƒ(r)=(r²+σ²)), a thin-plate spinefunction (e.g., ƒ(r)=r² log(r)), a piece-wise linear function (e.g.,ƒ(r)=½(|r+1|−|r−1|), or a cubic approximation function (e.g.,ƒ(r)=½(|r³+1|−|r³−1|)). In some examples, the parameter σ is a realparameter (e.g., a scaling parameter) and r is the distance between theinput signal (e.g., x₁(n), x₂(n), x₃(n), x_(N)(n) 502) and a vector ofthe nonlinear vector set. Processing elements 509 may be implemented,for example, using accumulation units that sum the intermediateprocessing results received from each of the processing elements 506. Incommunicating the intermediate processing results, each intermediateprocessing result may be weighted with a weight ‘W’. For example, themultiplication processing units may weight the intermediate processingresults based on a minimized error for the all or some of the adjustmentsignals that may generated by a neural network.

The processing elements 506 include a nonlinear vector set that may bedenoted as C_(i) (for i=1, 2, . . . H). H may represent the number ofprocessing elements 506. With the signals x₁(n), x₂(n), x₃(n), x_(N)(n)502 received as inputs to processing elements 506, after forwarding byprocessing elements 504, the output of the processing elements 506,operating as multiplication processing units, may be expressed ash_(i)(n), such that:

h _(i)(n)=ƒ_(i() ∥X(n)−C _(i)∥₎ (i=1,2, . . . ,H)  (5)

ƒ_(i) may represent a nonlinear function that is applied to themagnitude of the difference between x₁(n), x₂(n), x₃(n), x_(N)(n) 502and the center vectors C_(i). The output h_(i)(n) may represent anonlinear function such as a Gaussian function, multi-quadraticfunction, an inverse multi-quadratic function, a thin-plate spinefunction, or a cubic approximation function.

The output h_(i)(n) of the processing elements 506 may be weighted witha weight matrix ‘W’. The output h_(i)(n) of the processing elements 506can be referred to as intermediate processing results of the neuralnetwork 500. For example, the connection between the processing elements506 and processing elements 509 may be a linear function such that thesummation of a weighted output h_(i)(n) such that the output data y₁(n),y₂(n), y₃(n), y_(L)(n) 508 may be expressed, in Equation (6) as:

y _(i)(n)=Σ_(j=1) ^(H) W _(ij) h _(j)(n)=Σ_(j=1) ^(H) W _(ij)ƒ_(j()∥X(n)−C _(j)∥₎ (i=1,2, . . . ,L)  (6)

Accordingly, the output data y₁(n), y₂(n), y₃(n), y_(L)(n) 508 may bethe output y_(i)(n) of the i′th processing element 509 at time n, whereL is the number of processing elements 509. W_(ij) is the connectionweight between j′th processing element 506 and i′th processing element509 in the output layer. Advantageously, the output data y₁(n), y₂(n),y₃(n), y_(L)(n) 508, generated from the feedback signal X(n) 277, orx₁(n), x₂(n), x₃(n), x_(N)(n) 502, may be utilized by a DPM filter asfilter coefficient data, to compensate for (I/Q) imbalance or noiserelated thereto, e.g., the compensation may be performed to modifysubsequent input signals provided to a DAC or RF mixer in a manner thatcauses the output (e.g., a signal to be transmitted) to have a reducedpresence of (I/Q) imbalance or noise related thereto. Accordingly, awireless device or system that implements a neural network 500 maywholly and/or partially compensate for such (I/Q) imbalance.

While the neural network 500 has been described with respect to a singlelayer of processing elements 506 that include multiplication units, itcan be appreciated that additional layers of processing elements withmultiplication units may be added between the processing elements 504and the processing elements 509. The neural network is scalable inhardware form, with additional multiplication units being added toaccommodate additional layers. Using the methods and systems describedherein, additional layer(s) of processing elements includingmultiplication processing units and the processing elements 506 may beoptimized to determine the center vectors C_(i), and the connectionweights W_(ij) of each layer of processing elements includingmultiplication units. In some implementations, for example as describedwith reference to FIGS. 5C-5E, layers of processing elements 506 mayinclude multiplication/accumulation (MAC) units, with each layer havingadditional MAC units. Such implementations, having accumulated theintermediate processing results in a respective processing elements(e.g., the respective MAC unit), may also include memory look-up (MLU)units that are configured to retrieve a plurality of coefficients andprovide the plurality of coefficients as the connection weights for therespective layer of processing elements 506 to be mixed with the inputdata.

The neural network 500 can be implemented using one or more processors,for example, having any number of cores. An example processor core caninclude an arithmetic logic unit (ALU), a bit manipulation unit, amultiplication unit, an accumulation unit, a multiplication/accumulation(MAC) unit, an adder unit, a look-up table unit, a memory look-up unit,or any combination thereof. In some examples, the neural network 240 mayinclude circuitry, including custom circuitry, and/or firmware forperforming functions described herein. For example, circuitry caninclude multiplication unit, accumulation units, MAC units, and/or bitmanipulation units for performing the described functions, as describedherein. The neural network 240 may be implemented in any type ofprocessor architecture including but not limited to a microprocessor ora digital signal processor (DSP), or any combination thereof.

FIG. 5B is a schematic illustration of a recurrent neural networkarranged in accordance with examples described herein. The recurrentneural network 170 include three stages (e.g., layers): an inputs stage171; a combiner stage 173 and 175, and an outputs stage 177. While threestages are shown in FIG. 5B, any number of stages may be used in otherexamples, e.g., as described with reference to FIGS. 5C-5E. In someimplementations, the recurrent neural network 170 may have multiplecombiner stages such that outputs from one combiner stage is provided toanother combiners stage, until being providing to an outputs stage 177.As described with reference to FIG. 5C, for example, there may bemultiple combiner stages in a neural network 170. As depicted in FIG.5B, the delay units 175 a, 175 b, and 175 c may be optional componentsof the neural network 170. When such delay units 175 a, 175 b, and 175 care utilized as described herein, the neural network 170 may be referredto as a recurrent neural network. Accordingly, the recurrent neuralnetwork 170 can be used to implement any of the recurrent neuralnetworks described herein; for example, the recurrent neural network 280of FIG. 2 or, as described below, a recurrent neural network 840 of FIG.8, a recurrent neural network 1180 of FIG. 11, or a recurrent neuralnetwork 1294 of FIG. 12.

The first stage of the neural network 170 includes inputs node 171. Theinputs node 171 may receive input data at various inputs of therecurrent neural network. The second stage of the neural network 170 isa combiner stage including combiner units 173 a, 173 b, 173 c; and delayunits 175 a, 175 b, 175 c. Accordingly, the combiner units 173 and delayunits 175 may be collectively referred to as a stage of combiners.Accordingly, in the example of FIG. 5C with recurrent neural network 512implementing such combiners, such a recurrent neural network 512 canimplements the combiner units 173 a-c and delay units 175 a-c in thesecond stage. In such an implementation, the recurrent neural network512 may perform a nonlinear activation function using the input datafrom the inputs node 171 (e.g., input signals X1(n), X2(n), and X3(n)).The third stage of neural network 170 includes the outputs node 177.Additional, fewer, and/or different components may be used in otherexamples. Accordingly, the recurrent neural network 170 can be used toimplement any of the recurrent neural networks described herein; forexample, any of the recurrent neural networks 512 of FIGS. 5C-5E.

The recurrent neural network 170 includes delay units 175 a, 175 b, and175 c, which generate delayed versions of the output from the respectivecombiner units 173 a-c based on receiving such output data from therespective combiner units 173 a-c. In the example, the output data ofcombiner units 173 a-c may be represented as h(n); and, accordingly,each of the delay units 175 a-c delay the output data of the combinerunits 173 a-c to generate delayed versions of the output data from thecombiner units 173 a-c, which may be represented as h(n-t). In variousimplementations, the amount of the delay, t, may also vary, e.g., oneclock cycle, two clock cycles, or one hundred clock cycles. That is, thedelay unit 175 may receive a clock signal and utilize the clock signalto identify the amount of the delay. In the example of FIG. 5B, thedelayed versions are delayed by one time period, where 1″ represents atime period. A time period may corresponds to any number of units oftime, such as a time period defined by a clock signal or a time perioddefined by another element of the neural network 170. In variousimplementations, the delayed versions of the output from the respectivecombiner units 173 a-c may be referred to as signaling that is based onthe output data of combiner units 173 a-c.

Continuing in the example of FIG. 5B, each delay unit 175 a-c providesthe delayed versions of the output data from the combiner units 173 a-cas input to the combiner units 173 a-c, to operate, optionally, as arecurrent neural network. Such delay units 175 a-c may providerespective delayed versions of the output data from nodes of thecombiner units 173 a-c to respective input units/nodes of the combinerunits 173 a-c. In utilizing delayed versions of output data fromcombiner units 173 a-c, the recurrent neural network 170 may trainweights at the combiner units 173 a-c that incorporate time-varyingaspects of input data to be processed by such a recurrent neural network170. Once trained, in some examples, the inputs node 171 receiveswireless data to be transmitted from DAC(s) and/or mixer(s) as aplurality of feedback signals, such as, in the context of FIG. 12described below, DACs 1230 and/or mixers 1237 with a plurality offeedback signals X(n) 1249. Accordingly, the input nodes 171 receivesuch feedback signals X(n) 1249 and process that as input data in therecurrent neural network 170 a. Accordingly, because an RNN 170incorporates the delayed versions of output data from combiner units 173a-c, the time-varying nature of the input data may provide faster andmore efficient processing of the input data, such as feedback signalsX(n) 1249.

Examples of recurrent neural network training and inference can bedescribed mathematically. Again, as an example, consider input data at atime instant (n), given as: X(n)=[x_(i)(n),x₂(n), . . . x_(m)(n)]^(r).The center vector for each element in hidden layer(s) of the recurrentneural network 170 (e.g., combiner units 173 a-c) may be denoted asC_(i) (for i=1, 2, . . . , H, where H is the element number in thehidden layer). The output of each element in a hidden layer may then begiven as:

h _(i)(n)=ƒ_(i)(∥X(n)+h _(i)(n−t)−C _(i)∥) for (i=1,2, . . . , H)  (7)

t may be the delay at the delay unit 175 such that the output of thecombiner units 173 includes a delayed version of the output of thecombiner units 173. In some examples, this may be referred to asfeedback of the combiner units 173. Accordingly, each of the connectionsbetween a last hidden layer and the output layer may be weighted. Eachelement in the output layer may have a linear input-output relationshipsuch that it may perform a summation (e.g., a weighted summation).Accordingly, an output of the i′th element in the output layer at time nmay be written as:

$\begin{matrix}{{y_{i}(n)} = {{{\sum_{j = 1}^{H}{W_{ij}{h_{j}(n)}}} + {W_{ij}{h_{j}\left( {n - t} \right)}}} = {\sum_{j = 1}^{H}{W_{ij}{f_{j}\left( {{{X(n)} + {h_{i}\left( {n - t} \right)} - C_{j}}} \right)}}}}} & (8)\end{matrix}$

for (i=1, 2, . . . , L) and where L is the element number of the outputof the output layer and W_(ij) is the connection weight between the j′thelement in the hidden layer and the i′th element in the output layer.

Additionally or alternatively, while FIG. 5B has been described withrespect to a single stage of combiners (e.g., second stage) includingthe combiner units 173 a-c and delay units 175 a-c, it can beappreciated that multiple stages of similar combiner stages may beincluded in the neural network 170 with varying types of combiner unitsand varying types of delay units with varying delays, for example, aswill now be described with reference to FIGS. 5C-5E.

FIG. 5C is a schematic illustration of a recurrent neural network 512arranged in a system 501 in accordance with examples described herein.Such a hardware implementation (e.g., system 501) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, the neural network 500 of FIG. 5A orrecurrent neural network 170 of FIG. 5B. Additionally or alternatively,in some implementations, the recurrent neural network 512 may receiveinput data 510 a, 510 b, and 510 c from such a computing system. Theinput data 510 a, 510 b, and 510 c may be the feedback signal X(n) 277,which may be stored in a memory 545. In some examples, data stored inthe memory 545 may be a plurality of feedback signals X(n) 277. In anexample in which the electronic device 110 is coupled to the pluralityof antennas 101 and 103, the input data 510 a X₁(i, i−1) may correspondto a first feedback signal X(n) 277 based on a first RF transmission tobe transmitted at the antenna 101 at a first frequency; the input data510 b X₂(i, i−1) may correspond to a second feedback signal X(n) 277based on a second RF transmission to be transmitted at the antenna 103at a second frequency; and the input data 510 c X_(m)(i, i−1) maycorrespond to a m′th feedback signal X(n) 277 based on a m′th RFtransmission to be transmitted at an m′th antenna at a m′th frequency. mmay represent the number of antennas, with each antenna transmitting aportion of input data.

As denoted in the representation of the input data signals, the inputdata 510 a X₁(i, i−1) includes a current portion of the input data, attime i, and a previous portion of the input data, at time i−1. Forexample, a current portion of the input data may be a sample of a firstfeedback signal X(n) 277 at a certain time period (e.g., at time i),while a previous portion of the input data may be a sample of the firstfeedback signal X(n) 277 at a time period previous to the certain timeperiod (e.g., at time i−1). Accordingly, the previous portion of theinput data may be referred to as a time-delayed version of the currentportion of the input data. The portions of the input data at each timeperiod may be obtained in a vector or matrix format, for example. In anexample, a current portion of the input data, at time i, may be a singlevalue; and a previous portion of the input data, at time i−1, may be asingle value. Thus, the input data 510 a X₁(i, i−1) may be a vector. Insome examples, the current portion of the input data, at time i, may bea vector value; and a previous portion of the input data, at time i−1,may be a vector value. Thus, the input data 510 a X₁(i, i−1) may be amatrix.

Such input data, which is obtained with a current and previous portionof input data, may be representative of a Markov process, such that acausal relationship between at least the current sample and the previoussample may improve the accuracy of weight estimation for training of aplurality of coefficients to be utilized by the MAC units and MLUs ofthe recurrent neural network 512. As noted previously, the input data510 may represent data to be transmitted (e.g., amplified signals) at afirst frequency and/or data to be transmitted at a first wirelesschannel. Accordingly, the input data 510 b X2(i, i−1) may represent datato be transmitted at a second frequency or at a second wireless channel,including a current portion of the input data, at time i, and a previousportion of the input data, at time i−1. And, the number of input signalsto be transmitted by the recurrent neural network 512 may equal in someexamples to a number of antennas coupled to an electronic device 110implementing the recurrent neural network 512. Accordingly, the inputdata 510 c Xm(i, i−1) may represent data to be transmitted at a m′thfrequency or at a m′th wireless channel, including a current portion ofthe input data, at time i, and a previous portion of the input data, attime i−1.

The recurrent neural network 512 may include multiplicationunit/accumulation (MAC) units 511 a-c, 516 a-b, and 520; delay units 513a-c, 517 a-b, and 521; and memory lookup units (MLUs) 514 a-c, 518 a-b,and 522 that, when mixed with input data to be transmitted from thememory 545, may generate output data (e.g. B (1)) 530. Each set of MACunits and MLU units having different element numbers may be referred toas a respective stage of combiners for the recurrent neural network 512.For example, a first stage of combiners includes MAC units 511 a-c andMLUs 514 a-c, operating in conjunction with delay units 513 a-c, to forma first stage or “layer,” as referenced with respect to FIG. 5A having“hidden” layers as various combiner stages. Continuing in the example,the second stage of combiners includes MAC units 516 a-b and MLUs 518a-b, operating in conjunction with delay units 517 a-b, to form a secondstage or second layer of hidden layers. And the third stage of combinersmay be a single combiner including the MAC unit 520 and MLU 522,operating in conjunction with delay unit 521, to form a third stage orthird layer of hidden layers.

The recurrent neural network 512, may be provided instructions 515,stored at the mode configurable control 505, to cause the recurrentneural network 512 to configure the multiplication units 511 a-c, 516a-c, and 520 to multiply and/or accumulate input data 510 a, 510 b, and510 c and delayed versions of processing results from the delay units513 a-c, 517 a-b, and 521 (e.g., respective outputs of the respectivelayers of MAC units) with a plurality of coefficients to generate theoutput data 530 B(1). For example, the mode configurable control 505 mayexecute instructions that cause the memory 545 to provide a plurality ofcoefficients (e.g., weights and/or other parameters) stored in thememory 545 to the MLUs 514 a-c, 518 a-b, and 522 as weights for the MACunits 511 a-c, 516 a-b, and 520 and delay units 513 a-c, 517 a-b, and521.

As denoted in the representation of the respective outputs of therespective layers of MAC units (e.g., the outputs of the MLUs 514 a-c,518 a-b, and 522), the input data to each MAC unit 511 a-c, 516 a-b, and520 includes a current portion of input data, at time i, and a delayedversion of a processing result, at time i−1. For example, a currentportion of the input data may be a sample of a first feedback signalX(n) 277 at a certain time (e.g., at time i), while a delayed version ofa processing result may be obtained from the output of the delay units513 a-c, 517 a-b, and 521, which is representative of a time previous tothe certain time (e.g., as a result of the introduced delay).Accordingly, in using such input data, obtained from both a currentperiod and at least one previous period, output data B(1) 530 may berepresentative of a Markov process, such that a causal relationshipbetween at least data from a current time period and a previous timeperiod may be used to improve the accuracy of weight estimation fortraining of a plurality of coefficients to be utilized by the MAC unitsand MLUs of the recurrent neural network 512 or inference of signals tobe transmitted in utilizing the recurrent neural network 512. As notedpreviously, the input data 510 may represent various feedback signalsX(n) 277. And, the number of input signals obtained by the recurrentneural network 512 may equal in some examples to a number of antennascoupled to an electronic device 110 implementing the recurrent neuralnetwork 512. Accordingly, the input data 510 c Xm(i, i−1) may representdata obtained at a m′th frequency or at a m′th wireless channel,including a current portion of the input data, at time i. Accordingly,in utilizing delayed versions of output data from 513 a-c, 517 a-b, and521 the recurrent neural network 170 provides individualizedfrequency-band, time-correlation data for processing of signals to betransmitted.

In an example of executing such instructions 515 for mixing input datawith coefficients, at a first layer of the MAC units 511 a-c and MLUs514 a-c, the multiplication unit/accumulation units 511 a-c areconfigured to multiply and accumulate at least two operands fromcorresponding input data 510 a, 510 b, or 510 c and an operand from arespective delay unit 513 a-c to generate a multiplication processingresult that is provided to the MLUs 514 a-c. For example, themultiplication unit/accumulation units 511 a-c may perform amultiply-accumulate operation such that three operands, M N, and T aremultiplied with respective coefficients of a plurality of coefficients,and then added with P to generate a new version of P that is stored inits respective MLU 514 a-c. Accordingly, the MLU 514 a latches themultiplication processing result, until such time that the storedmultiplication processing result is be provided to a next layer of MACunits. The MLUs 514 a-c, 518 a-b, and 522 may be implemented by anynumber of processing elements that operate as a memory look-up unit suchas a D, T, SR, and/or JK latches.

Additionally in the example, the MLU 514 a provides the processingresult to the delay unit 513 a. The delay unit 513 a delays theprocessing result (e.g., h1(i)) to generate a delayed version of theprocessing result (e.g., h1(i−1)) to output to the MAC unit 511 a asoperand T. While the delay units 513 a-c, 517 a-b, and 521 of FIG. 5Care depicted introducing a delay of ‘1’, it can be appreciated thatvarying amounts of delay may be introduced to the outputs of first layerof MAC units. For example, a clock signal that introduced a sample delayof ‘1’ (e.g., h1(i−1)) may instead introduce a sample delay of ‘2’, ‘4’,or ‘100’. In various implementations, the delay units 513 a-c, 517 a-b,and 521 may correspond to any number of processing units that canintroduce a delay into processing circuitry using a clock signal orother time-oriented signal, such as flops (e.g., D-flops) and/or one ormore various logic gates (e.g., AND, OR, NOR, etc. . . . ) that mayoperate as a delay unit.

In the example of a first hidden layer of a recurrent neural network,the MLUs 514 a-c may retrieve a plurality of coefficients stored in thememory 545, which may be weights associated with weights to be appliedto the first layer of MAC units to both the data from the current periodand data from a previous period (e.g., the delayed versions of firstlayer processing results). For example, the MLU 514 a can be a tablelook-up that retrieves one or more coefficients of a plurality ofcoefficients to be applied to both operands M and N, as well as anadditional coefficient to be applied to operand T. The MLUs 514 a-c alsoprovide the generated multiplication processing results to the nextlayer of the MAC units 516 a-b and MLUs 518 a-b. The additional layersof the MAC units 516 a, 516 b and MAC unit 520 working in conjunctionwith the MLUs 518 a, 518 b and MLU 522, respectively, may continue toprocess the multiplication results to generate the output data 530 B(n).Using such a circuitry arrangement, the output data 530 B(1) may begenerated from the input data 510 a, 510 b, and 510 c.

Advantageously, the recurrent neural network 512 of system 501 mayutilize a reduced number of MAC units and/or MLUs, e.g., as compared tothe recurrent neural network 512 of FIG. 5D. As described above, thenumber of MAC units and MLUs in each layer of the recurrent neuralnetwork 512 is associated with a number of channels and/or a number ofantennas coupled to a device in which the recurrent neural network 512is being implemented. For example, the first layer of the MAC units andMLUs may include m number of those units, where m represents the numberof antennas. Each subsequent layer may have a reduced portion of MACunits, delay units, and MLUs. As depicted, in FIG. 5C for example, asecond layer of MAC units 516 a-b, delay unit 517 a-b, and MLUs 518 a-bmay include m−1 MAC units and MLUs, when m=3. Accordingly, the lastlayer in the recurrent neural network 512, including the MAC unit 520,delay unit 521, and MLU 522, includes only one MAC, one delay unit, andone MLU. Because the recurrent neural network 512 utilizes input data510 a, 510 b, and 510 c that may represent a Markov process, the numberof MAC units and MLUs in each subsequent layer of the processing unitmay be reduced, without a substantial loss in precision as to the outputdata 530 B(1); for example, when compared to a recurrent neural network512 that includes the same number of MAC units and MLUs in each layer,like that of recurrent neural network 512 of system 550.

The plurality of coefficients, for example from memory 545, can be mixedwith the input data 510 a-510 c and delayed version of processingresults to generate the output data 530 B(1). For example, therelationship of the plurality of coefficients to the output data 530B(1) based on the input data 510 a-c and the delayed versions ofprocessing results may be expressed as:

B(1)=a ¹*ƒ(Σ_(j=1) ^(m-1) a ^((m-1))ƒ_(j)(Σ_(k=1) ^(m) a ^((m)) X_(k)(i)))  (9)

where a(m), a(m−1), a1 are coefficients for the first layer ofmultiplication/accumulation units 511 a-c and outputs of delay units 513a-c; the second layer of multiplication/accumulation units 516 a-b andoutputs of delay units 517 a-b; and last layer with themultiplication/accumulation unit 520 and output of delay unit 521,respectively; and where ƒ(•) is the mapping relationship which may beperformed by the memory look-up units 514 a-c and 518 a-b. As describedabove, the memory look-up units 514 a-c and 518 a-b retrievecoefficients to mix with the input data and respective delayed versionsof each layer of MAC units. Accordingly, the output data may be providedby manipulating the input data and delayed versions of the MAC unitswith the respective multiplication/accumulation units using a set ofcoefficients stored in the memory. A plurality of coefficients may beassociated with vectors representative of (I/Q) imbalance or noiserelated thereto. A plurality of coefficients may be based on connectionweights obtained from the training of a recurrent neural network (e.g.,recurrent neural network 280 or 1180). The resulting mapped data may bemanipulated by additional multiplication/accumulation units andadditional delay units using additional sets of coefficients stored inthe memory associated with the desired wireless protocol. The sets ofcoefficients multiplied at each stage of the recurrent neural network512 may represent or provide an estimation of the processing of theinput data in specifically-designed hardware (e.g., an FPGA).

Further, it can be shown that the system 501, as represented by Equation(9), may approximate any nonlinear mapping with arbitrarily small errorin some examples and the mapping of system 501 may be determined by thecoefficients a(m), a(m−1), a1. For example, if such coefficients of aplurality of coefficients are specified, any mapping and processingbetween the input data 510 a-510 c and the output data 530 may beaccomplished by the system 501. For example, the plurality ofcoefficients may represent nonlinear mappings of the input data 510 a-cto the output data B(1) 530. In some examples, the nonlinear mappings ofthe plurality of coefficients may represent a Gaussian function, apiece-wise linear function, a sigmoid function, a thin-plate-splinefunction, a multi-quadratic function, a cubic approximation, an inversemulti-quadratic function, or combinations thereof. In some examples,some or all of the memory look-up units 514 a-c, 518 a-b may bedeactivated. For example, one or more of the memory look-up units 514a-c, 518 a-b may operate as a gain unit with the unity gain. Such arelationship, as derived from the circuitry arrangement depicted insystem 501, may be used to train an entity of the computing system 501to generate the plurality of coefficients. For example, using Equation(9), an entity of the computing system 501 may compare input data to theoutput data to generate the plurality of coefficients.

Each of the multiplication unit/accumulation units 511 a-c, 516 a-b, and520 may include multiple multipliers, multiple accumulation unit, orand/or multiple adders. Any one of the multiplication unit/accumulationunits 511 a-c, 516 a-b, and 520 may be implemented using an ALU. In someexamples, any one of the multiplication unit/accumulation units 511 a-c,516 a-b, and 520 can include one multiplier and one adder that eachperform, respectively, multiple multiplications and multiple additions.The input-output relationship of a multiplication/accumulation unit 511a-c, 516 a-b, and 520 may be represented as:

$\begin{matrix}{B_{out} = {\overset{I}{\sum\limits_{i = 1}}{C_{i}*{B_{i\; n}(i)}}}} & (10)\end{matrix}$

where “I” represents a number to perform the multiplications in thatunit, C_(i) the coefficients which may be accessed from a memory, suchas memory 545, and N_(in)(i) represents a factor from either the inputdata 510 a-c or an output from multiplication unit/accumulation units511 a-c, 516 a-b, and 520. In an example, the output of a set ofmultiplication unit/accumulation units, B_(out), equals the sum of aplurality of coefficients, C_(i) multiplied by the output of another setof multiplication unit/accumulation units, B_(in)(i). B_(in)(i) be theinput data such that the output of a set of multiplicationunit/accumulation units, B_(out), equals the sum of a plurality ofcoefficients, C_(i) multiplied by input data.

While described in FIG. 5C as a recurrent neural network 512, it can beappreciated that the recurrent neural network 512 may be implemented inor as any of the recurrent neural networks described herein, inoperation to cancel and/or compensate (I/Q) imbalance via thecalculation of such noise as implemented in a recurrent neural network.For example, the recurrent neural network 512 can be used to implementany of the recurrent neural networks described herein; for example, therecurrent neural network 280 of FIG. 2, recurrent neural network 840 ofFIG. 8, recurrent neural network 1180 of FIG. 11, or recurrent neuralnetwork 1294 of FIG. 12. In such implementations, recurrent neuralnetworks may be used to reduce and/or improve errors which may beintroduced by I/Q imbalance or nonlinear power amplifier noise.Advantageously, with such an implementation, wireless systems anddevices implementing such RNNs may increase capacity of their respectivewireless networks because additional data may be transmitted in suchnetworks, which would not otherwise be transmitted due to the effects ofI/Q imbalance or nonlinear power amplifier noise.

FIG. 5D is a schematic illustration of a recurrent neural network 512arranged in a system 550 in accordance with examples described herein.Such a hardware implementation (e.g., system 550) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, the neural network 500 of FIG. 5A, orrecurrent neural network 170 of FIG. 5B. Similarly described elements ofFIG. 5D may operate as described with respect to FIG. 5C, but may alsoinclude additional features as described with respect to FIG. 5D. Forexample, FIG. 5D depicts MAC units 562 a-c and delay units 563 a-c thatmay operate as described with respect MAC units 51 la-c and delay units513 a-c of FIG. 5C. Accordingly, elements of FIG. 5D, whose numericalindicator is offset by 50 with respect to FIG. 5C, include similarlyelements of the recurrent neural network 512; e.g., MAC unit 566 aoperates similarly with respect to MAC unit 516 a. The system 550,including recurrent neural network 512, also includes additionalfeatures not highlighted in the recurrent neural network 512 of FIG. 5C.For example, the recurrent neural network 512 of FIG. 5D additionallyincludes MAC units 566 c and 570 b-c; delay units 567 c and 571 b-c; andMLUs 568 c and 572 b-c, such that the output data is provided as 575a-c, rather than as singularly in FIG. 5C as B(1) 530. Advantageously,the system 550 including a recurrent neural network 512 may process theinput data 560 a-c to generate the output data 575 a-c with greaterprecision. For example, the recurrent neural network 512 may process theinput data 560 a-560 c with additional coefficient retrieved at MLU 568c and multiplied and/or accumulated by additional MAC units 566 c and570 b-c and additional delay units 567 c and 571 b-c, to generate outputdata 575 a-c with greater precision. In implementations where boardspace (e.g., a printed circuit board) is not a primary factor in design,implementations of the recurrent neural network 512 of FIG. 5D may bedesirable as compared to that of recurrent neural network 512 of FIG.5C; which, in some implementations may occupy less board space as aresult of having fewer elements than the recurrent neural network 512 ofFIG. 5D.

FIG. 5E is a schematic illustration of a recurrent neural network 512arranged in a system 580 in accordance with examples described herein.Such a hardware implementation (e.g., system 580) may be used, forexample, to implement one or more neural networks, such as the recurrentneural network 240 of FIG. 2, neural network 500 of FIG. 5A, orrecurrent neural network 170 of FIG. 5B. Similarly described elements ofFIG. 5E may operate as described with respect to FIG. 5D, except for thedelay units 563 a-c, 567 a-c, and 571 a-c of FIG. 5D. For example, FIG.5E depicts MAC units 582 a-c and delay units 583 a-c that may operate asdescribed with respect to MAC units 562 a-c and delay units 563 a-c ofFIG. 5D. Accordingly, elements of FIG. 5E, whose numerical indicator isoffset by 20 with respect to FIG. 5D, include similarly elements of therecurrent neural network 512: e.g., MAC unit 586 a operates similarlywith respect to MAC unit 566 a.

The system 580, including recurrent neural network 512, also includesadditional features not highlighted in the recurrent neural network 512of FIG. 5D. Different than FIG. 5D, FIG. 5E depicts delay units 583 a,583 b, and 583 c. Accordingly, the processing unit of FIG. 5E illustratethat recurrent neural network 512 may include varying arrangements tothe placement of the inputs and outputs of delay units, as illustratedwith delay units 583 a, 583 b, and 583 c. For example, the output ofMLUs 588 b may be provided to delay unit 583 b, to generate a delayedversion of that processing result from the second layer of MAC units, asan input to the first layer of MAC units, e.g., as an input to MAC unit582 b. Accordingly, the recurrent neural network 512 of system 580 isillustrative that delayed versions of processing results may be providedas inputs to other hidden layers, different than the recurrent neuralnetwork 512 of system 550 in FIG. 5D showing respective delayed versionsbeing provided as inputs to the same layer in which those delayedversions were generated (e.g., the output of MLU 568 b is provided todelay unit 567 b, to generate a delayed version for the MAC unit 566 bin the same layer from which the processing result was outputted).Therefore, in the example, even the output B(n) 595 c may be provided,from the last hidden layer, to the first hidden layer (e.g., as an inputto MAC unit 582 c).

Advantageously, such delayed versions of processing results, which maybe provided as inputs to different or additional hidden layers, maybetter compensate “higher-order” memory effects in a recurrent neuralnetwork 170 that implements one or more recurrent neural network 512 ofFIG. 5E, e.g., as compared to the recurrent neural network 512 of FIG.5C or 5D. For example, higher-order memory effects model the effects ofleading and lagging envelope signals used during training of therecurrent neural network 170, to provide output data that estimates avector representative of (I/Q) imbalance. In the example, a recurrentneural network 170 that estimates that vector (e.g., a Volterra seriesmodel) may include varying delayed versions of processing results thatcorresponds to such leading and lagging envelopes (e.g., of variousenvelopes encapsulating the vector). Accordingly, implementing therecurrent neural network 512 incorporates such higher-order memoryeffects, e.g., for an inference of a recurrent neural network 170, toprovide output data 595 a-c based on input data 581 a-c.

While described in FIGS. 5D and 5E respectively describe a recurrentneural network 512, it can be appreciated that the recurrent neuralnetwork 512 or combinations thereof may be implemented in or as any ofthe recurrent neural networks described herein, in operation to canceland/or compensate (I/Q) imbalance via the calculation of such noise asimplemented in a recurrent neural network. In such implementations,recurrent neural networks may be used to reduce and/or improve errorswhich may be introduced by (I/Q) imbalance. Advantageously, with such animplementation, wireless systems and devices implementing such RNNs mayincrease capacity of their respective wireless networks becauseadditional data may be transmitted in such networks, which would nototherwise be transmitted due to the effects of (I/Q) imbalance.

In the following, FIGS. 11 and 12 will first be further described beforeFIGS. 6-10. FIG. 11 is a schematic illustration of an electronic device1100 arranged in accordance with examples described herein. Theelectronic device 1100 includes a baseband transmitter 1115 with acorresponding transmitter path to/from transmitting antenna (Tx) 1150.The electronic device 1100 includes a baseband receiver 1185 with acorresponding receiver path to/from receiving antenna (Tx) 1155. Theelectronic device 1100 may be implemented as the electronic device 100,110 with any of the wireless transmitters and any of the wirelessreceivers of FIG. 1. The electronic device 1100 may be implemented asthe electronic device 100, 110 with a wireless transmitter 131, 133 anda wireless receiver 115, 117 of FIG. 1. The electronic device 1100 maybe implemented as the electronic device 100, 110 with a wirelesstransmitter 111, 113 and a wireless receiver 135, 137 of FIG. 1.Similarly numbered elements of FIG. 11 may operate as described withrespect to respectively numbered elements of FIG. 2, but may alsoinclude additional elements as described with respect to FIG. 11. Forexample, the electronic device 1100 includes a digital to analogconverter (DAC) 1130, an intermediate frequency (IF) filter 1135, amixer 1137. The electronic device 1100 may provide a local oscillatorsignal from a local oscillator 1190. The electronic device 1100 mayinclude a power amplifier 1140, which may provide an amplified signal tobe transmitted T(n) 1147.

After having received a signal to be transmitted t(n) 1110, the basebandtransmitter 1115 may perform baseband processing on that signal to betransmitted t(n) 1110. The baseband processing on that signal to betransmitted t(n) 1110 may be performed by the baseband transmitter 1115to generate a baseband signal to be transmitted t(n) 1116. The signal1116 is provided to the recurrent neural network 1180 and also provided,along the transmitter path towards the transmitting antenna 1150. Forexample, the signal 1116 provided to the recurrent neural network 1180may be provided to a DPM filter 1118. The DPM filter 1118 at leastpartially compensates the signal t(n) 1116 based on a model includingfilter coefficient data (e.g., a plurality of coefficients) provided tothe DPM filter 1118 by the recurrent neural network 1180. The DPM filter1118 utilizes the model based on the filter coefficient data to at leastpartially compensate the signal 1116 for noise in the electronic device1100 by the model utilized by the DPM filter 1118. For example, thenoise in the signal 1116 for which the electronic device 1100 at leastpartially compensates includes I/Q imbalance or mismatch. The filtercoefficient data may be selected to reduce the error introduced into thesignal to be transmitted t(n) 1116 by I/Q imbalance or mismatch, whenthat signal 1116 is provided for transmission at the transmittingantenna 1150. For example, with the electronic device 1100 having such arecurrent neural network 1180, the DPM filter 1118 may provide,advantageously, noise compensation for the signal to be transmitted t(n)1100 due to I/Q imbalance or mismatch that the signal may experience asit is processed for transmission as the amplified signal to betransmitted T(n) 1147. Continuing in the processing of the signal to betransmitted t(n) 1110, after having been at least partially compensatedfor noise by the DPM filter 1118, that signal to be transmitted t(n)1110 may be further processed along the transmitter path towards thetransmitting antenna 1150.

The DPM filter 1118 utilizes the provided filter coefficient data towholly and/or partially compensate for I/Q imbalance present in thesignal to be transmitted t(n) 1110, e.g., prior to input of additionalinput signals to be provided to the DAC 1130 or mixer 1137. Whilereferred to as the DPM filter 1118, it can be appreciated that variousdigital filters may implement the DPM filter 1118, and thus the DPMfilter 1118 may also be referred to as a digital filter.

In addition to being provided by the recurrent neural network 1180 tothe DPM filter 1118, the baseband signal to be transmitted t(n) 1116 maybe provided by the recurrent neural network 1180 along the transmitterpath towards the transmitting antenna 1150. For example, the basebandsignal to be transmitted t(n) 1116 may be provided by the recurrentneural network 1180 to the DPD filter 1120. The DPD filter 1120 at leastpartially compensates the signal t(n) 1116 based on a model includingfilter coefficient data (e.g., a plurality of coefficients) provided tothe DPD filter 1120 by the recurrent neural network 1180. The DPD filter1120 may utilize the model based on the filter coefficient data to atleast partially compensate the signal 1116 for noise in the electronicdevice 1100. The noise in the signal 1116 for which the electronicdevice 1100 at least partially compensates includes nonlinear poweramplifier noise generated by the power amplifier 1140. The filtercoefficient data may be selected to reduce the error introduced into thesignal to be transmitted t(n) 1116 by nonlinear power amplifier noise,when that signal 1116 is amplified by power amplifier 1140 fortransmission at the transmitting antenna 1150. By reducing the errorintroduced into the signal to be transmitted t(n) 1116 by nonlinearpower amplifier noise, when that signal 1116 is amplified by poweramplifier 1140 for transmission at the transmitting antenna 1150,nonlinear behavior of the power amplifier and efficiency of the poweramplifier 1140 may be improved in some examples.

After the DPD filter 1120 is at least partially compensates noise in thesignal to be transmitted t(n) 1116, the signal 1116 may be furtherprocessed along the transmitter path towards the transmitting antenna1150. The electronic device 1100 may include a transmitter path from theDPD filter 1120 towards the transmitting antenna 1150 along which thecompensated signal 1116 is processed that is a first communication path.The first communication path of FIG. 11 may be similar to the firstcommunication path of FIG. 2.

The switch 1145 may be activated by a control signal (e.g., a selectionsignal) that indicates an uplink transmission time interval (TTI) isoccurring in a time division duplexing configured radio frame that theelectronic device 1100 utilizes. The switch 1145 may be similar to theswitch 245 as previously described for the electronic device 200. Whenthe switch 1145 is activated, the amplified signal to be transmittedT(n) 1147 may be provided to the receiver path of the electronic device1100 to be used as a feedback signal in calculations performed by therecurrent neural network 1180. The amplified signal to be transmittedT(n) 1147 may be provided to the receiver path as the signal X(n) 1149,to provide a feedback signal X(n) 1177 to the recurrent neural network1180. The electronic device 1100 may include a receiver path from theswitch 1145 towards baseband receiver 1185 along which the signal X(n)1149 is processed that is a second communication path. The secondcommunication path of FIG. 11 may be similar to the second communicationpath previously described for the electronic device 200.

After receiving the feedback signal X(n) 1177, the recurrent neuralnetwork 1180 may calculate an error signal between the signal to betransmitted t(n) 1116 and the compensated wireless transition signal,which may be used to reduce error in a model of the DPM filter 1118and/or the DPD filter 1120. The filter coefficient data B(n) 1143 (e.g.,a plurality of coefficients) may be provided to the DPD filter 1120.Additionally or alternatively, the filter coefficient data B′(n) 1142(e.g., a plurality of coefficients) may be provided to the DPM filter1118. The recurrent neural network may utilize the error signal todetermine and/or update filter coefficient data B′(n) 1142 (e.g., aplurality of coefficients) and/or filter coefficient data B(n) 1143(e.g., a plurality of coefficients). The DPM filter 1118 may at leastpartially compensate for I/Q imbalance or mismatch. The DPD filter 1120may at least partially compensate for non-linear power amplifier noise.

With regard to the filter coefficient data B′(n) 1142, the DPM filter1118 may utilize that data to compensate for noise introduced into thesignal to be transmitted t(n) 1110 due to I/Q imbalance or mismatch. Forexample, I/Q imbalance or mismatch may be introduced into the signalwhile a signal is being transmitted, due to a mismatch in eitheramplitude or phase between the I/Q branches. Signals having the I/Qimbalance or mismatch may result in the I/Q branches beingnon-orthogonal. The mismatch in amplitude or phase may result in amirror image problem which causes in-band interference signals and maycompromise performance. The I/Q mismatch (e.g., digital mismatch) mayoccur in only a single transmission channel affecting other transmissionchannels, or may occur in more than one transmission channel. Effects ofimpairments due to I/Q imbalance or mismatch may become more pronouncedas higher order modulated waveforms and/or more wideband multichannelsignals are used. The demodulation performance of the signals may beaffected by I/Q imbalance or mismatch unless I/Q correction isimplemented in some examples.

For the recurrent neural network 1180 to calculate the plurality ofcoefficients, the recurrent neural network 1180 may compute an errorsignal which may be used to reduce a difference between the signal to betransmitted t(n) 1116 that is input to the DPD filter 1120 and thefeedback signal X(n) 1177. The feedback signal X(n) 1177 may be providedby a feedback path to the recurrent neural network 1180. The feedbacksignal X(n) 1177 may be then provided by the recurrent neural network1180 towards the transmitting antenna 1150 to the DPM filter 1118 and/orthe DPD filter 1120. Accordingly, the signal X(n) 1149 may be processedby a feedback receiving line from the LNA 1160, the mixer 1163 inconjunction with the provided local oscillator signal from the localoscillator 1190, the intermediate frequency (IF) filter 1165, theanalog-to-digital converter 1170, and the numerically controlledoscillator (NCO) 1175. Accordingly, the processed signal X(n) 1149 maythen be used to generate the feedback signal X(n) 1177.

The receiver path from the LNA 1160 for the electronic device 1100 alongwhich the signal X(n) 1149 is processed may be different from thefeedback path from the LNA 1160 along which the signal X(n) 1149 isprocessed to generate the feedback signal X(n) 1177. The feedback pathfrom the LNA 1160 for the electronic device 1100 along which the signalX(n) 1149 is processed may include the recurrent neural network 1180,but not the baseband received signal 1187. The receiver path from theLNA 116 for the electronic device 1100 along which the signal X(n) 1149is processed may include the baseband received signal 1187, but not therecurrent neural network 1180.

After receiving the feedback signal X(n) 1177, the recurrent neuralnetwork 1180 may calculate an error signal between the signal to betransmitted t(n) 1116 and the compensated wireless transition signal.The error signal calculated by the recurrent neural network 1180 may beused to reduce error in a model of the DPM filter 1118. The recurrentneural network may utilize the error signal to determine and/or updatefilter coefficient data B′(n) 1142 (e.g., a plurality of coefficients)provided to the DPM filter 1118. The determined and/or updated filtercoefficient data B′(n) 1142 is for utilization in a model of the DPMfilter 1118 that at least partially compensates I/Q imbalance ormismatch. For the recurrent neural network 1180 to calculate theplurality of coefficients, the recurrent neural network 1180 may computean error signal for reducing a difference between the signal to betransmitted t(n) 1116 as it is provided to the DPM filter 1118 and thefeedback signal X(n) 1177. For example, the recurrent neural network1180 may store previous versions of the feedback signal X(n) 1177. Therecurrent neural network 1180 may utilize an algorithm to reduce (e.g.,minimize) the difference between the signal to be transmitted t(n) 1116and the feedback signal X(n) 1177. For example, the algorithm to reducethe difference may be a least-mean-squares (LMS) algorithm,least-squares (LS) algorithm, and/or total-least-squares (TLS)algorithm. Accordingly, in reducing the difference between the signal tobe transmitted t(n) 1116 and the feedback signal X(n) 1177, therecurrent neural network may determine the filter coefficient data B′(n)1142 to be utilized in the DPM filter 1118. In some implementations,sample vectors may be utilized, instead of the signal to be transmittedt(n) 1116. For example, the sample vectors may be utilized to determinean initial set of the filter coefficient data B′(n) 1142.

In some examples, the recurrent neural network 1180 may determine thefilter coefficient data B′(n) 1142 to be utilized in the DPM filter 1118as a “memoryless” system. The new filter coefficient data used in the“memoryless” system replaces filter coefficient data that the DPM filter1118 utilized before receiving the filter coefficient data B′(n) 1142.Updating the DPM filter 1118 with the filter coefficient data B′(n) 1142may be referred to as optimizing the filter coefficient data. Updatingthe DPM filter 1118 with the new filter coefficient data may includeupdating some or all of the filter coefficient data filter coefficientdata B′(n) 1142.

The filter coefficient data B′(n) 1142 to be utilized in the DPM filter1118 may be determined by the recurrent neural network 1180. Therecurrent neural network 1180 may determine coefficients based on adifference between a signal to be transmitted t(n) 1116 and a feedbacksignal X(n) 1177.

In an analogous manner as shown and described with reference to FIG. 2,the same receiver path may be utilized at least in part for processing areceived signal and generating a feedback signal. For example, theelectronic device 1100 in FIG. 11 may utilize less board space and/orresources as compared to an electronic device that utilized a separatepath for the feedback signal and processing of a received signal. Anelectronic device that includes separate paths for providing thefeedback signal and processing the received signal may need separateradio frequency (RF) mixers, separate intermediate frequency (IF)filters, separate analog-to-digital converters, and separate numericallycontrolled oscillators (NCO's) for each path. In contrast, thosecomponent(s) may be shared by the single path in FIG. 11 used to bothgenerate the feedback signal and process the received signal.

For example, the electronic device 1100 may utilize several componentsfor both generating a feedback signal, e.g., feedback signal X(n) 1177and processing a received signal, e.g., received signal R(n) 1157. Thecomponents which may be used for both operations in the electronicdevice 1100 may include, for example, the LNA 1160, the mixer 1163, theintermediate frequency (IF) filter 1165, the analog-to-digital converter1170, and the numerically controlled oscillator (NCO) 1175. The switch1145 may be used to control when the components are used to generate thefeedback signal and when they are used to process the received signal.

The switch 1145 may be deactivated by a control signal indicative of adownlink TTI occurring in a radio frame (e.g., a time division duplexedradio frame). When the switch 1145 is deactivated, a feedback signalX(n) 1149 may not be provided to the receiver path of the electronicdevice 1100. With the switch 1145 deactivated, the received signal R(n)1157 may be provided to the receiver path of the electronic device 1100.The received signal R(n) may be processed in the receiver path, forexample to generate the baseband received signal 1187. The receivedsignal R(n) 1157 may be provided to the receiver path, for example atthe low noise amplifier (LNA) 1160. Accordingly, the received signalR(n) 1157 may be processed by the LNA 1160, the mixer 1163, theintermediate frequency (IF) filter 1165, the analog-to-digital converter1170, the numerically controlled oscillator (NCO) 1175, and the basebandreceiver 1185 to generate the baseband received signal 1187.Accordingly, the electronic device 1100 may utilize the same receiverpath that is used to process a received signal to generate a feedbacksignal for the recurrent neural network 1180. Utilizing the receiverpath for both purposes may facilitate efficient use of computationalresources and/or board space of the electronic device 1100.

The switch 1145 may be deactivated and activated during the downlinkinterval and the uplink interval, respectively. The deactivated switchduring the downlink time period (e.g., downlink interval or downlinkTTI) may provide the received signal R(n) 1157 to the receiver path ofthe electronic device 1100. The activated switch during the uplink timeperiod (e.g., uplink interval or uplink TTI) may provide the signal X(n)1149 to the receiver path of the electronic device 110. Accordingly, thesame receiver path of electronic device 1100 may be utilized forprocessing received wireless signals during downlink time periods andproviding feedback signals to the recurrent neural network during uplinktime periods.

In some examples, the recurrent neural network 1180, while not beingprovided a feedback signal X(n) 1177 during the downlink time period,may calculate and/or determine filter coefficient data while thereceived signal R(n) 1157 is being processed. Accordingly, inconjunction with time division duplexing configured radio frames, theelectronic device 1100 may utilize a single receiver path to provideboth the feedback signal X(n) 1177 and to process wireless transmissionsignals. The feedback signal X(n) 1177 provided while the wirelesstransmission signals are processed is provided to the recurrent neuralnetwork 1180. The wireless transmission signals processed while thefeedback signal X(n) 1177 is provided may be the received signal R(n)1157 used to provide baseband received signals r(n) 1187.

In some examples, the transmitter path may include the DPM filter 1118,but not the DPD filter 1120. For example, the I/Q imbalance or mismatchmay be compensated by the DPM filter 1118 even if the DPD filter 1120 isnot included in the transmitter path. In some examples, the transmitterpath may include the DPD filter 1120 but not the DPM filter 1118. Forexample, the nonlinear power amplifier noise may be compensated by theDPD filter 1120 even if the DPM filter 1118 is not included in thetransmitter path.

The device of FIG. 11 includes certain components that may be used toimplement certain components of the system shown in FIG. 1 in someexamples. For example, FIG. 11 depicts the baseband transmitter 1115,the DPD 1120, and the power amplifier 1140 that may be included in thesystem 100 of FIG. 1. For example, FIG. 11 depicts the baseband receiver1185 coupled to the low noise amplifier (LNA) 1160 that may be includedin the system 100 of FIG. 1. Such features may also be implemented inthe electronic device 102, 110 of FIG. 1. For example, the descriptionsof the baseband transmitter 1115 in FIG. 11 are interchangeable as thewireless transmitter 131, 133 of the electronic device 102 or thewireless transmitter 111, 113 of the electronic device 110 asimplemented throughout the examples described herein. For example, thedescriptions of the baseband receiver 1185 in FIG. 11 areinterchangeable as the descriptions of the wireless receiver 135, 137 ofthe electronic device 102 or the wireless receiver 115, 117 of theelectronic device 110 as implemented throughout the examples describedherein. Therefore, it can be appreciated that, while referring to asingle wireless transmitter 1115, the wireless transmitter 1115 may beone of multiple wireless transmitters 131, 133, like in FIG. 1, to alsohave the same features as described with respect a single wirelesstransmitter 1115. It can be appreciated that, while referring to asingle wireless receiver 1185 in FIG. 11, the wireless receiver 1185 maybe one of multiple wireless receivers 135, 137 and 115, 117, like inFIG. 1, to also have the same features as described with respect thesingle wireless receiver 1185. While referring to a single wirelessreceiver 1185 in FIG. 11, the wireless receiver 1185 may be one ofmultiple wireless transmitters 131, 133 and 111, 113, like in FIG. 1, toalso have the same features as described with respect the singlewireless receiver 1185.

In some examples, the order of the DPD filter 1120 and the DPM filter1118 in the transmitter path of the electronic device 1200 is reversed.When the order of the DPM filter 1118 and the DPD filter 1120 isreversed, the signal 1116 is provided to the recurrent neural network1180 and also provided to the DPD filter 1120 and then to the DPM filter1118. The first communication path coupled to the DPM filter 1118 thatis coupled after the DPD filter 1120 of FIG. 11 may be similar to thefirst communication path of FIG. 2.

In some examples, the electronic device 1100 may operate with the switch1145 deactivated during the downlink interval; and activated during theuplink interval. In such a case, the electronic device 1100 may operateas user equipment (UE). The amplified signal to be transmitted T(n) 1147may be provided, during the uplink time period, to the transmittingantenna 1150 of the user equipment. The user equipment, during thedownlink time period, may provide the received signal R(n) 1157 receivedby the receiving antenna 1155 to the receiver path of the electronicdevice 1100.

In some examples, other devices may be used as the electronic device1100 in place of the user equipment. For example, the electronic device1100 may be a base station. The switch 1145 in the base station may bedeactivated during the uplink interval; and activated during thedownlink interval. The amplified signal to be transmitted T(n) 1147 maybe provided during the downlink time period to the transmitting antenna1150 of the base station. The base station during the uplink time periodmay provide the received signal R(n) 1157 received by the receivingantenna 1155 to the receiver path of the electronic device 1100.

In some embodiments, various devices may also be implemented as a UE, ashas been described with respect to FIG. 11. For example, the electronicdevice 200, previously described with reference to FIG. 2, may beimplemented as UE used to transmit the amplified signal to betransmitted T(n) 247 during the uplink time period and receive thereceived signal R(n) 257 during the downlink time period. Or, theelectronic device 200 may be a base station used to transmit theamplified signal to be transmitted T(n) 247 during the downlink timeperiod and receive the received signal R(n) 257 during the uplink timeperiod.

FIG. 12 is a schematic illustration of an electronic device 1200arranged in accordance with examples described herein. The electronicdevice 1200 includes a wireless transmitter, the wireless transmitterincluding a DPM filter 1218, a digital to analog (D/A) converter 1230, amixer 1237, a I/Q local oscillator 1290, a signal generator 1292, and arecurrent neural network 1294. In the D/A converter 1230, (a) is a firstD/A converter circuit for an I branch of the wireless transmitter and(b) is a second D/A converter circuit for a Q branch of the wirelesstransmitter. In the mixer 1237, (a) is a first mixer circuit for the Ibranch of the wireless transmitter and (b) is a second mixer circuit forthe Q branch of the wireless transmitter. The electronic device 1200 maybe used to provide a signal to be transmitted t(n) 1210. In the signalto be transmitted t(n) 1210, (a) is a first portion of the signal 1210for an I branch of the wireless transmitter and (b) is a second portionof the signal 1210 for the Q branch of the wireless transmitter. Theelectronic device 1200 may be used to implement the electronic device100 and/or 110 of FIG. 1, including the wireless transmitter 131, 133and/or wireless transmitter 111, 113. Similarly numbered elements ofFIG. 12 may operate as described with respect to respectively numberedelements of FIG. 11, but may also include additional features and becoupled to other elements as described with respect to FIG. 12

The recurrent neural network 1294 may be coupled to a recurrent neuralnetwork. The recurrent neural network coupled to the recurrent neuralnetwork 1294 may be used to implement the recurrent neural network 1180of FIG. 11. In some examples, the recurrent neural network may beintegrated within the recurrent neural network 1294. The wirelesstransmitter of the electronic device 1200 may include a transmittingantenna. The transmitting antenna may be used to implement thetransmitting antenna 1150 of FIG. 11.

After having received a signal to be transmitted t(n) 1210, the wirelesstransmitter of the electronic device 1200 may perform basebandprocessing on that signal to be transmitted t(n) 1210 to generate abaseband signal to be transmitted. The baseband signal to be transmittedmay be used to implement the baseband signal to be transmitted t(n) 1116of FIG. 11. The signal to be transmitted may be provided to therecurrent neural network of the recurrent neural network 1294, and alsoprovided, along the transmitter path towards a transmitting antenna, tothe DPM filter 1218. In some examples, the electronic device 1200 mayinclude a transmitter path from the DPM filter 1218 towards the basebandtransmitting antenna along which the signal to be transmitted isprocessed that is a first communication path. The first communicationpath coupled to the DPM filter 1218 of FIG. 12 may be similar to thefirst communication path of FIG. 11.

The D/A converter 1230 and the mixer 1237 in conjunction with the localoscillator 1290 and the signal generator 1292 may process the signal tobe transmitted by the transmitting antenna. The local oscillator 1290may provide a local oscillating signal used by the mixer 1237 to processthe signal to be transmitted by the transmitting antenna. The mixer 1237in combination with the local oscillator 1290 and the signal generator1292 may output the feedback signal X(n) 1249. The feedback signal X(n)1249 may be output based on an amplified signal generated within thesignal generator 1292 (e.g., an amplified signal generated by a poweramplifier in the signal generator 1292).

After receiving the feedback signal X(n) 1249, the recurrent neuralnetwork 1294 may generate a feedback signal provided to the recurrentneural network coupled to the recurrent neural network 1294. Thefeedback signal provided to the recurrent neural network may beimplemented by the feedback signal X(n) 1177 provided to the recurrentneural network 1180 of FIG. 11. In some examples, the recurrent neuralnetwork 1294 may include an intermediate frequency (IF) filter, ananalog-to-digital converter, and a numerically controlled oscillator(NCO) used to provide the feedback signal to the recurrent neuralnetwork. The intermediate frequency (IF) filter, the analog-to-digitalconverter, and the numerically controlled oscillator (NCO) of FIG. 12may be used to implement the intermediate frequency (IF) filter 1165,the analog-to-digital converter 1170, and the numerically controlledoscillator (NCO) 1175 of FIG. 11. After receiving the feedback signalX(n) 1249, the recurrent neural network may compute an error signalbetween the signal to be transmitted t(n) 1210 and a compensatedwireless transition signal.

The DPM filter 1218 may at least partially compensate I/Q imbalance ormismatch in the signal to be transmitted t(n) 1210 by using filtercoefficient data provided by the recurrent neural network. The signal inwhich the I/Q imbalance or mismatch is at least partially compensatedmay be generated according to the signal to be transmitted t(n) 1210.The signal in which the I/Q imbalance or mismatch is at least partiallycompensated of FIG. 12 may be used to implement the signal to betransmitted t(n) 1116 of FIG. 11. The filter coefficient data may beused to implement the filter coefficient data B′(n) 1142 of FIG. 11. Theerror signal computed by the recurrent neural network 1294 may be usedto reduce error in a model of the DPM filter 1218. The recurrent neuralnetwork may utilize the error signal to determine and/or update a filtercoefficient data B′(n) 1242 (e.g., a plurality of coefficients) providedto the DPM filter 1218 for use in a model of the DPM filter 1218. TheDPM filter 1218 may implement a model selected to at least partiallycompensate I/Q imbalance or mismatch. For the recurrent neural networkcalculate the plurality of coefficients, the recurrent neural networkmay compute an error signal reflecting a difference between the signalto be transmitted t(n) 1210 and the feedback signal. For example, thedifference may be reduced (e.g., minimized) by utilizing Equation (11):

Z(n)=Z _(R)(n)+jZ _(I)(n)  (11)

The first portion (a) and the second portion (b) of the signal to betransmitted t(n) 1210 corresponding to the I branch and the Q branch maybe represented in Equation (11) as Z(n). Z(n) includes respective realand imaginary parts Z_(R)(n) and Z_(I)(n). The first and second portions(a) and (b) of the signal to be transmitted t(n) 1210 may be representedin Equation (11) Z_(R)(n) and Z_(I)(n), respectively. The signal to betransmitted t(n) 1210 may be converted by the digital to analog (D/A)converter 1230 to generate a converted signal. The converted signal maybe corrected by the recurrent neural network. For example, the convertedsignal output by the digital to analog (D/A) converters 1230 may becorrected by utilizing Equation (12):

Z ^(pre)(n)=Z(n)+W(n)Z*(n)  (12)

Equation (12) indicates that a pre-matched signal, Z^(pre)(n), may be asum of the signal to be transmitted and a product of weights (e.g.,W(n)) and a conjugate of the signal to be transmitted, Z*(n).

The recurrent neural network may find desired weights (e.g., W(n)) byusing a minimization equation of an expected value of Z(n) minus afeedback path Z^(TX)(n), as follows:

W(n)=min E|Z(n)−Z ^(TX)(n)²|  (13)

The filter coefficient data B′(n) 1242 may be calculated in accordancewith Equation (13) as W(n). The feedback signal provided to therecurrent neural network may be calculated in accordance with Equation(13) as Z^(TX)(n). The DPM filter 1218 may at least partially compensatefor I/Q imbalance or mismatch using the filter coefficient data B′(n)1242. The I/Q imbalance or mismatch may be compensated in the DPM filter1218 responsive to the feedback signal provided to the recurrent neuralnetwork.

In generating a baseband received signal, the electronic device 1200 mayutilize the same receiver path that is utilized to generate and providethe feedback signal provided to the recurrent neural network. Use of thesame path may make more efficient use of computational resources and/orboard space of the electronic device 1200 as compared to a device havingseparate paths for each purpose. Accordingly, the same receiver path ofelectronic device 1200 is utilized for the receiving wireless signalsduring downlink time periods and providing feedback signals to therecurrent neural network 1294 to generate the filter coefficient dataB′(n) 1242 during uplink time periods. The recurrent neural network 1294may accordingly be a dual-purpose circuit that acts as both a recurrentneural network and a receive circuit depending on whether or not thesignal generator 1292 (e.g., the switch in the signal generator) isactivated for providing the feedback signal X(n) 1249 to the recurrentneural network 1294. In some examples, the recurrent neural network1294, while not generating the filter coefficient data B′(n) 1242 duringthe downlink time period, may calculate and/or determine filtercoefficient data during at least a portion of time (e.g., while) areceived signal is being processed. In this manner, the electronicdevice 1200 may utilize a single receiver path to provide both thefilter coefficient data B′(n) 1242 and to receive and process wirelesstransmission signals to provide baseband received signals.

FIG. 6 is a schematic illustration of a time frame 600 for a TDDtransmission time interval (TTI) arranged in accordance with examplesdescribed herein. The time frame 600 includes downlink TTIs 601, 604,and 605. The time frame also includes uplink TTIs 603. The time frame600 also includes special time frames 602, which may include additionaluplink and/or downlink TTIs for special TDD time periods. For example, aspecial time period may be allocated in the time frame 600 for specificfunctionalities of a wireless protocol, such as signaling/handshaking.The downlink TTIs may be of varying time period lengths, as depicted,with the downlink TTI 604 being thrice as long as the downlink TTI 601.

The time frame 600 may be utilized in time-division duplexing configuredradio frames for electronic devices described herein. For example, withrespect to the electronic device 1100, described above the switch 1145activates a path to provide the feedback signal X(n) 1177 through thewireless receiver path to the recurrent neural network 1180, when thewireless receiver path is not receiving an active wireless signal. Forexample, the wireless receiver path may not receive an active wirelesssignal during the uplink TTIs 603. Accordingly, during the uplink TTIs603, the switch 1145 may be activated to provide the feedback signalX(n) 1177 through the wireless receiver path to the recurrent neuralnetwork 1180. In providing the feedback over multiple uplink TTIs 603,the recurrent neural network 1180 may provide the filter coefficientdata of a model that at least partially compensate for I/Q imbalance.Additionally or alternatively, during at least a portion of downlinkTTIs 601, 604, and 605, the switch may deactivate the path that providesfeedback signal X(n) 1177 through the wireless receiver path, so thatthe wireless receiver portion of a wireless transceiver may receivewireless transmission signals R(n) 1157, thereby providing for efficientTDD configured radio frames to both provide the feedback signal X(n)1177 to the recurrent neural network 1180 and to receive wirelesssignals R(n) 1157 using the same wireless receiver path.

FIG. 7A is a schematic illustration of an I/Q imbalance compensationmethod 700 in accordance with examples described herein. Example method700 may be implemented using, for example, electronic device 102, 110 ofFIG. 1, recurrent neural network 512 of FIGS. 5C-5E, electronic device800 of FIG. 8, electronic device 1100 of FIG. 11, electronic device 1200of FIG. 12, or any system or combination of the systems depicted in theFigures described herein, such as in conjunction with a time frame 600of FIG. 6. The operations described in blocks 708-728 may also be storedas computer-executable instructions in a computer-readable medium.

Example method 700 may begin with block 708 that starts execution of theI/Q imbalance compensation method and includes generating, in a transmitpath of a transceiver, a transmission signal based on an input signal tobe transmitted. In the example, a transceiver includes transmit andreceive paths from respective transmitting and receiving antennas, suchas the electronic device 1100, as described above. In context of FIG.11, the signal to be transmitted T(n) 1147 is provided, in a transmitpath of the electronic device 1100, to the transmitting antenna 1150through a DAC 1130 or mixer 1137. Accordingly, in generating thetransmission signal based on the transmission signal, the signal to betransmitted T(n) 1147 is processed by the DAC 1130 and the mixer 1137,which can introduce error into the signal to be transmitted T(n) 1147due to I/Q imbalance.

Block 708 may be followed by block 712, such that the method furtherincludes activating, a switch path based at least in part on receiving aselection signal. The transmit path to the transmitting antenna 1150includes a path through the switch 1145 for transmission of any signalto be transmitted. For example, to activate the switch 1145, the switch1145 receives a selection signal that indicates the switch path is to beactivated when a transmission time interval (TTI) of a time-divisionduplex (TDD) configured radio frame is designated for an uplinktransmission. When the switch 1145 is activated, the receive path of thetransceiver is coupled to the transmit path of the transceiver.Accordingly, that same amplified signal to be transmitted T(n) 1147 isprovided to a receive path via the switch 1145, when the switch 1145 isactivated, as the signal X(n) 1149.

Block 712 may be followed by block 716, such that the method furtherincludes, after activating the switch path, providing the transmissionsignal as feedback to a recurrent neural network. In the context of FIG.11, after processing of the signal X(n) 1149, a feedback signal X(n)1177 is provided to the recurrent neural network 1180. For example, withthe switch 1145 having been activated, the receive path of thetransceiver is coupled to the transmit path of the transceiver.Accordingly, the same signal that is provided as the transmission signalT(n) 1147 to the antenna 1150 is also provided to the receive path ofthe transceiver, e.g., beginning at the LNA 1160.

Block 716 may be followed by block 720, such that the method furtherincludes mixing, at the recurrent neural network, the feedback as inputdata using respective pluralities of coefficients to generate outputdata. For example, various ALUs, such as multiplication/accumulationprocessing units, may be configured to operate as the circuitry of FIGS.5C-5E. Accordingly, to mix the feedback signal X(n) 1177, a recurrentneural network 1180 receives the feedback signal X(n) 1177 as input data(e.g., input data 510 a, 510 b, and 510 c). In some implementations, therecurrent neural network 1180 may implement the method 700 such that therecurrent neural network calculates first processing results based onthe input data and delayed versions of respective outputs of a firstlayer of multiplication/accumulation processing units (MAC units) withthe plurality of coefficients; and calculates output data based on thefirst processing results and delayed versions of respective outputs of arespective additional layer of MAC units with the additional pluralityof coefficients. In mixing the feedback signal X(n) 1177 as input datausing respective pluralities of coefficients at the recurrent neuralnetwork 1180, output data is generated to be utilized as filtercoefficient data in DPM filter 1118 (e.g., as a model for at leastpartially compensating I/Q imbalance noise).

Block 720 may be followed by block 724, such that the method furtherincludes filtering another transmission signal in accordance with theoutput data, prior to processing at a digital-to-analog converter (DAC)or mixer. To filter another transmission signal in accordance with theoutput data, the output data is provided, from the recurrent neuralnetwork 1180, as filter coefficient data to the DPM filter 1118 that atleast partially compensates for I/Q imbalance in additional transmissionsignals to be transmitted by the electronic device 200. Accordingly,when an additional signal to be transmitted t(n) 210 is to betransmitted in the electronic device 200, the DPM filter 1118 filtersthe transmission signal in accordance with the output data that wasprovided as filter coefficient data to the DPM filter 1118 from therecurrent neural network 1180, prior to input at the DAC 1130 or mixer1137, where processing of RF signals without such compensation of an I/Qimbalance may increase noise in the transmission signal T(n) 1147. Block724 may be followed by block 728 that ends the example method 700.

The blocks included in the described example method 700 is forillustration purposes. In some embodiments, these blocks may beperformed in a different order. In some other embodiments, variousblocks may be eliminated. In still other embodiments, various blocks maybe divided into additional blocks, supplemented with other blocks, orcombined together into fewer blocks. Other variations of these specificblocks are contemplated, including changes in the order of the blocks,changes in the content of the blocks being split or combined into otherblocks, etc.

FIG. 7B is a flowchart of a method 750 in accordance with examplesdescribed herein. Example method 750 may be implemented using, forexample, electronic device 102, 110 of FIG. 1, recurrent neural network512 of FIGS. 5C-5E, electronic device 800 of FIG. 8, electronic device1100 of FIG. 11, electronic device 1200 of FIG. 12, or any system orcombination of the systems depicted in the Figures described herein,such as in conjunction with a time frame 600 of FIG. 6. The operationsdescribed in blocks 754-778 may also be stored as computer-executableinstructions in a computer-readable medium such as the mode configurablecontrol 505, storing the executable instructions 515.

Example method 750 may begin with a block 754 that starts execution ofthe mixing input data with coefficient data routine. The method mayinclude a block 758 recites “retrieving a plurality of coefficients froma memory.” As described herein, a memory look-up (MLU) units can beconfigured to retrieve a plurality of coefficients and provide theplurality of coefficients as the connection weights for a respectivelayer of processing elements of a recurrent neural network. For example,the memory may store (e.g., in a database) a plurality of coefficientsrepresentative of (I/Q) imbalance. In the implementation of recurrentneural network 512 of FIG. 5C, for example, the MLU 514 a can be a tablelook-up that retrieves one or more pluralities of coefficients to beapplied to both operands M and N, as well as an additional coefficientto be applied to operand T. Accordingly, MLUs of the recurrent neuralnetwork may request the pluralities of coefficients from a memory partof the implementing computing device, from a memory part of an externalcomputing device, or from a memory implemented in a cloud-computingdevice. In turn, the plurality of coefficients may be retrieved from thememory as utilized by the recurrent neural network.

Block 758 may be followed by block 762 that recites “obtaining inputdata associated with a transmission to be processed at a recurrentneural network.” The input data may correspond to amplified signalsx1(n), x2(n) 221, 223 that is received as input data at a recurrentneural network 1180 or any of the recurrent neural networks describedherein. Block 762 may be followed by block 766 that recites“calculating, at a first layer of multiplication/accumulation processingunits (MAC units) of the recurrent neural network, the input data anddelayed versions of respective outputs of the first layer of MAC unitswith the plurality of coefficients to generate first processingresults.” As described herein, the recurrent neural network utilizes theplurality of coefficients such that mixing the coefficients with inputdata and delayed versions of respective outputs of the first layer ofMAC units generates output data that reflects the processing of theinput data with coefficients by the circuitry of FIG. 5C, 5D, or 5E. Forexample, various ALUs in an integrated circuit may be configured tooperate as the circuitry of FIG. 5C, 5D, or 5E, thereby mixing the inputdata and delayed versions of respective outputs of the first layer ofMAC units with the coefficients as described herein. For example, withreference to FIG. 5C, the input data and delayed versions of respectiveoutputs of the first layer of MAC units may be calculated with theplurality of coefficients to generate first processing results, at afirst layer of multiplication/accumulation processing units (MAC units).In some examples, various hardware platforms may implement the circuitryof FIG. 5C, 5D, or 5E, such as an ASIC, a DSP implemented as part of aFPGA, or a system-on-chip.

Block 766 may be followed by block 770 that recites “calculating, atadditional layers of MAC units, the first processing results and delayedversions of at least a portion of the first processing results with theadditional plurality of coefficients to generate second processingresults.” As described herein, the recurrent neural network utilizesadditional plurality of coefficients such that mixing the coefficientswith certain processing results and delayed versions of at least aportion of those certain processing results generates output data thatreflects the processing of the input data with coefficients by thecircuitry of FIG. 5C, 5D, or 5E. For example, with reference to FIG. 5C,the processing results of the first layer (e.g., multiplicationprocessing results) and delayed versions of at least a portion of thoseprocessing results may be calculated with the additional plurality ofcoefficients to generate second processing results, at a second layer ofmultiplication/accumulation processing units (MAC units). The processingresults of the second layer may be calculated with an additionalplurality of coefficients to generate the output data B(1) 530.

Block 770 may be followed by block 774 that recites “providing, from therecurrent neural network, output data as filter coefficient data.” Asdescribed herein, the output data may be provided from the recurrentneural network 1180 to the DPM filter 1118 to compensate or reduce I/Qimbalance noise. Advantageously, output data from a recurrent neuralnetwork 1180 (e.g., y1(n), y2(n), y3(n), yL(n) 508) based at leastpartly on the feedback signal X(n) 1177 is utilized by the DPM filter1118, as filter coefficient data, e.g., prior to input of additionalinput signals to be transmitted to a power amplifier. Accordingly, awireless device or system that implements the method 750 may compensateor reduce I/Q imbalance noise increased by processing of signal to betransmitted t(n) 1110 at the DAC 1130 or the mixer 1137. Block 774 maybe followed by block 778 that ends the example method 750.

The blocks included in the described example methods 700 and 750 are forillustration purposes. In some embodiments, these blocks may beperformed in a different order. In some other embodiments, variousblocks may be eliminated. In still other embodiments, various blocks maybe divided into additional blocks, supplemented with other blocks, orcombined together into fewer blocks. Other variations of these specificblocks are contemplated, including changes in the order of the blocks,changes in the content of the blocks being split or combined into otherblocks, etc.

FIG. 8 is a block diagram of an electronic device 800 arranged inaccordance with examples described herein. The electronic device 800 mayoperate in accordance with any example described herein, such aselectronic device 102, 110 of FIG. 1, electronic device 200 of FIG. 2,recurrent neural network 512 of FIGS. 5C-5E, or any system orcombination of the systems depicted in the Figures described herein,such as in conjunction with a time frame 600 of FIG. 6. The electronicdevice 800 may be implemented in a smartphone, a wearable electronicdevice, a server, a computer, an appliance, a vehicle, or any type ofelectronic device. For example, FIGS. 9-12 describe various devices thatmay be implemented as the electronic device 800 using a recurrent neuralnetwork 840 to make inference results or train on data acquired from thesensor 830. Generally, recurrent neural networks (e.g., recurrent neuralnetwork 840) may make inference results based on data acquired from thesensor 830. For example, in addition to using a RNN for compensating orreducing (I/Q) imbalance or noise related thereto, an electronic device800 may use an RNN to make inference results regarding datasets acquiredvia a sensor 830 or via a data network 895. Making an inference resultsmay include determining a relationship among one or more datasets, amongone or more subsets of a dataset, or among different subsets of variousdatasets. For example, a determined relationship may be represented asan AI model that the RNN generates. Such a generated AI model may beutilized to make predictions about similar datasets or similar subsetsof a dataset, when provided as input to such an AI model. As one skilledin the art can appreciate, various types of inference results may bemade by a RNN, which could include generating one or more AI modelsregarding acquired datasets.

The sensor 830 included in the electronic device 800 may be any sensorconfigured to detect an environmental condition and quantify ameasurement parameter based on that environmental condition. Forexample, the sensor 830 may be a photodetector that detects light andquantifies an amount or intensity of light that is received or measuredat the sensor 830. A device with a sensor 830 may be referred to as asensor device. Examples of sensor devices include sensors for detectingenergy, heat, light, vibration, biological signals (e.g., pulse, EEG,EKG, heart rate, respiratory rate, blood pressure), distance, speed,acceleration, or combinations thereof. Sensor devices may be deployed onbuildings, individuals, and/or in other locations in the environment.The sensor devices may communicate with one another and with computingsystems which may aggregate and/or analyze the data provided from one ormultiple sensor devices in the environment.

The electronic device 800 includes a computing system 802, a recurrentneural network 840, an I/O interface 870, and a network interface 890coupled to a data network 895. The data network 895 may include anetwork of data devices or systems such as a data center or otherelectronic devices 800 that facilitate the acquisition of data sets orcommunicate inference results among devices coupled to the data network895. For example, the electronic device 800 may communicate inferenceresults about (I/Q) imbalance, which are calculated at the recurrentneural network 840, to communicate such inference results to otherelectronic devices 800 with a similar connection or to a data centerwhere such inference results may be utilized as part of a data set,e.g., for further training in a machine learning or AI system.Accordingly, in implementing the recurrent neural network 840 in theelectronic device 800, mobile and sensor devices that operate as theelectronic device 800 may facilitate a fast exchange of inferenceresults or large data sets that are communicated to data center fortraining or inference.

The computing system 802 includes a wireless transceiver 810. Thewireless transceiver may include a wireless transmitter and/or wirelessreceiver, such as wireless transmitter 300 and wireless receiver 400.Recurrent neural network 840 may include any type of microprocessor,central processing unit (CPU), an application specific integratedcircuits (ASIC), a digital signal processor (DSP) implemented as part ofa field-programmable gate array (FPGA), a system-on-chip (SoC), or otherhardware to provide processing for device 800.

The computing system 802 includes memory units 850 (e.g., memory look-upunit), which may be non-transitory hardware readable medium includinginstructions, respectively, for calculating filter coefficient data orbe memory units for the retrieval, calculation, or storage of filtercoefficient data. The memory units 850 may be utilized to store datasets from machine learning or AI techniques executed by the electronicdevice 800. The memory units 850 may also be utilized to store,determine, or acquire inference results for machine learning or AItechniques executed by the electronic device 800. In some examples, thememory units 850 may include one or more types of memory, including butnot limited to: DRAM, SRAM, NAND, or 3D XPoint memory devices.

The computing system 802 may include control instructions that indicatewhen to execute such stored instructions for calculating filtercoefficient data or for the retrieval or storage of filter coefficientdata. Upon receiving such control instructions, the recurrent neuralnetwork 840 may execute such control instructions and/or executing suchinstructions with elements of computing system 802 (e.g., wirelesstransceiver 810) to perform such instructions. For example, suchinstructions may include a program that executes the method 700, aprogram that executes the method 750, or a program that executes bothmethods 700 and 750. In some implementations, the control instructionsinclude memory instructions for the memory units 850 to interact withthe recurrent neural network 840. For example, the control instructionsmay include an instruction that, when executed, facilitates theprovision of a read request to the memory units 850 to access (e.g.,read) a large dataset, such as one or more feedback signals X(n) 277, todetermine an inference result. As another example, the controlinstructions may include an instruction that, when executed, facilitatesthe provision of a write request to the memory units 850 to write a dataset that the electronic device 800 has acquired, e.g., via the sensor830 or via the I/O interface 870. Control instructions may also includeinstructions for the memory units 850 to communicate data sets orinference results to a data center via data network 895. For example, acontrol instruction may include an instruction that memory units 850write data sets or inference results about (I/Q) imbalance, which arecalculated at the recurrent neural network 840, to a cloud server at adata center where such inference results may be utilized as part of adata set, e.g., for further training in a machine learning or AI systemor for the processing of further communication signals (e.g., filtercoefficient data for the RNN 1180 or recurrent neural network 1294).

Communications between the recurrent neural network 840, the I/Ointerface 870, and the network interface 890 may be provided via aninternal bus 880. The recurrent neural network 840 may receive controlinstructions from the I/O interface 870 or the network interface 890,such as instructions to determine and provide filter coefficient data toa digital mismatch filter (e.g., DPM filter 1118). For example, the I/Ointerface 870 may facilitate a connection to a camera device that obtainimages and communicates such images to the electronic device 800 via theI/O interface 870.

Bus 880 may include one or more physical buses, communicationlines/interfaces, and/or point-to-point connections, such as PeripheralComponent Interconnect (PCI) bus, a Gen-Z switch, a CCIX interface, orthe like. The I/O interface 870 can include various user interfacesincluding video and/or audio interfaces for the user, such as a tabletdisplay with a microphone. Network interface 890 communications withother electronic devices, such as electronic device 800 or acloud-electronic server, over the data network 895. For example, thenetwork interface 890 may be a USB interface.

FIG. 9 illustrates an example of a wireless communications system 900 inaccordance with aspects of the present disclosure. The wirelesscommunications system 900 includes a base station 910, a mobile device915, a drone 917, a small cell 930, and vehicles 940, 945. The basestation 910 and small cell 930 may be connected to a network thatprovides access to the Internet and traditional communication links. Thesystem 900 may facilitate a wide-range of wireless communicationsconnections in a 5G system that may include various frequency bands,including but not limited to: a sub-6 GHz band (e.g., 700 MHzcommunication frequency), mid-range communication bands (e.g., 2.4 GHz),mmWave bands (e.g., 24 GHz), and a NR band (e.g., 3.5 GHz).

Additionally or alternatively, the wireless communications connectionsmay support various modulation schemes, including but not limited to:filter bank multi-carrier (FBMC), the generalized frequency divisionmultiplexing (GFDM), universal filtered multi-carrier (UFMC)transmission, bi-orthogonal frequency division multiplexing (BFDM),sparse code multiple access (SCMA), non-orthogonal multiple access(NOMA), multi-user shared access (MUSA), and faster-than-Nyquist (FTN)signaling with time-frequency packing. Such frequency bands andmodulation techniques may be a part of a standards framework, such asLong Term Evolution (LTE) (e.g., 1.8 GHz band) or other technicalspecification published by an organization like 3GPP or IEEE, which mayinclude various specifications for subcarrier frequency ranges, a numberof subcarriers, uplink/downlink transmission speeds, TDD/FDD, and/orother aspects of wireless communication protocols.

The system 900 may depict aspects of a radio access network (RAN), andsystem 900 may be in communication with or include a core network (notshown). The core network may include one or more serving gateways,mobility management entities, home subscriber servers, and packet datagateways. The core network may facilitate user and control plane linksto mobile devices via the RAN, and it may be an interface to an externalnetwork (e.g., the Internet). Base stations 910, communication devices920, and small cells 930 may be coupled with the core network or withone another, or both, via wired or wireless backhaul links (e.g., S1interface, X2 interface, etc.).

The system 900 may provide communication links connected to devices or“things,” such as sensor devices, e.g., solar cells 937, to provide anInternet of Things (“IoT”) framework. Connected things within the IoTmay operate within frequency bands licensed to and controlled bycellular network service providers, or such devices or things may. Suchfrequency bands and operation may be referred to as narrowband IoT(NB-IoT) because the frequency bands allocated for IoT operation may besmall or narrow relative to the overall system bandwidth. Frequencybands allocated for NB-IoT may have bandwidths of, 50, 100, 150, or 200kHz, for example.

Additionally or alternatively, the IoT may include devices or thingsoperating at different frequencies than traditional cellular technologyto facilitate use of the wireless spectrum. For example, an IoTframework may allow multiple devices in system 900 to operate at a sub-6GHz band or other industrial, scientific, and medical (ISM) radio bandswhere devices may operate on a shared spectrum for unlicensed uses. Thesub-6 GHz band may also be characterized as and may also becharacterized as an NB-IoT band. For example, in operating at lowfrequency ranges, devices providing sensor data for “things,” such assolar cells 937, may utilize less energy, resulting in power-efficiencyand may utilize less complex signaling frameworks, such that devices maytransmit asynchronously on that sub-6 GHz band. The sub-6 GHz band maysupport a wide variety of uses case, including the communication ofsensor data from various sensors devices. Examples of sensor devicesinclude sensors for detecting energy, heat, light, vibration, biologicalsignals (e.g., pulse, EEG, EKG, heart rate, respiratory rate, bloodpressure), distance, speed, acceleration, or combinations thereof.Sensor devices may be deployed on buildings, individuals, and/or inother locations in the environment. The sensor devices may communicatewith one another and with computing systems which may aggregate and/oranalyze the data provided from one or multiple sensor devices in theenvironment.

In such a 5G framework, devices may perform functionalities performed bybase stations in other mobile networks (e.g., UMTS or LTE), such asforming a connection or managing mobility operations between nodes(e.g., handoff or reselection). For example, mobile device 915 mayreceive sensor data from the user utilizing the mobile device 915, suchas blood pressure data, and may transmit that sensor data on anarrowband IoT frequency band to base station 910. In such an example,some parameters for the determination by the mobile device 915 mayinclude availability of licensed spectrum, availability of unlicensedspectrum, and/or time-sensitive nature of sensor data. Continuing in theexample, mobile device 915 may transmit the blood pressure data becausea narrowband IoT band is available and can transmit the sensor dataquickly, identifying a time-sensitive component to the blood pressure(e.g., if the blood pressure measurement is dangerously high or low,such as systolic blood pressure is three standard deviations from norm).

Additionally or alternatively, mobile device 915 may formdevice-to-device (D2D) connections with other mobile devices or otherelements of the system 900. For example, the mobile device 915 may formRFID, WiFi, MultiFire, Bluetooth, or Zigbee connections with otherdevices, including communication device 920 or vehicle 945. In someexamples, D2D connections may be made using licensed spectrum bands, andsuch connections may be managed by a cellular network or serviceprovider. Accordingly, while the above example was described in thecontext of narrowband IoT, it can be appreciated that otherdevice-to-device connections may be utilized by mobile device 915 toprovide information (e.g., sensor data) collected on different frequencybands than a frequency band determined by mobile device 915 fortransmission of that information.

Moreover, some communication devices may facilitate ad-hoc networks, forexample, a network being formed with communication devices 920 attachedto stationary objects and the vehicles 940, 945, without a traditionalconnection to a base station 910 and/or a core network necessarily beingformed. Other stationary objects may be used to support communicationdevices 920, such as, but not limited to, trees, plants, posts,buildings, blimps, dirigibles, balloons, street signs, mailboxes, orcombinations thereof. In such a system 900, communication devices 920and small cell 930 (e.g., a small cell, femtocell, WLAN access point,cellular hotspot, etc.) may be mounted upon or adhered to anotherstructure, such as lampposts and buildings to facilitate the formationof ad-hoc networks and other IoT-based networks. Such networks mayoperate at different frequency bands than existing technologies, such asmobile device 915 communicating with base station 910 on a cellularcommunication band.

The communication devices 920 may form wireless networks, operating ineither a hierarchal or ad-hoc network fashion, depending, in part, onthe connection to another element of the system 900. For example, thecommunication devices 920 may utilize a 700 MHz communication frequencyto form a connection with the mobile device 915 in an unlicensedspectrum, while utilizing a licensed spectrum communication frequency toform another connection with the vehicle 945. Communication devices 920may communicate with vehicle 945 on a licensed spectrum to providedirect access for time-sensitive data, for example, data for anautonomous driving capability of the vehicle 945 on a 5.9 GHz band ofDedicated Short Range Communications (DSRC).

Vehicles 940 and 945 may form an ad-hoc network at a different frequencyband than the connection between the communication device 920 and thevehicle 945. For example, for a high bandwidth connection to providetime-sensitive data between vehicles 940, 945, a 24 GHz mmWave band maybe utilized for transmissions of data between vehicles 940, 945. Forexample, vehicles 940, 945 may share real-time directional andnavigation data with each other over the connection while the vehicles940, 945 pass each other across a narrow intersection line. Each vehicle940, 945 may be tracking the intersection line and providing image datato an image processing algorithm to facilitate autonomous navigation ofeach vehicle while each travels along the intersection line. In someexamples, this real-time data may also be substantially simultaneouslyshared over an exclusive, licensed spectrum connection between thecommunication device 920 and the vehicle 945, for example, forprocessing of image data received at both vehicle 945 and vehicle 940,as transmitted by the vehicle 940 to vehicle 945 over the 24 GHz mmWaveband. While shown as automobiles in FIG. 9, other vehicles may be usedincluding, but not limited to, aircraft, spacecraft, balloons, blimps,dirigibles, trains, submarines, boats, ferries, cruise ships,helicopters, motorcycles, bicycles, drones, or combinations thereof.

While described in the context of a 24 GHz mmWave band, it can beappreciated that connections may be formed in the system 900 in othermmWave bands or other frequency bands, such as 28 GHz, 37 GHz, 38 GHz,39 GHz, which may be licensed or unlicensed bands. In some cases,vehicles 940, 945 may share the frequency band that they arecommunicating on with other vehicles in a different network. Forexample, a fleet of vehicles may pass vehicle 940 and, temporarily,share the 24 GHz mmWave band to form connections among that fleet, inaddition to the 24 GHz mmWave connection between vehicles 940, 945. Asanother example, communication device 920 may substantiallysimultaneously maintain a 700 MHz connection with the mobile device 915operated by a user (e.g., a pedestrian walking along the street) toprovide information regarding a location of the user to the vehicle 945over the 5.9 GHz band. In providing such information, communicationdevice 920 may leverage antenna diversity schemes as part of a massiveMIMO framework to facilitate time-sensitive, separate connections withboth the mobile device 915 and the vehicle 945. A massive MIMO frameworkmay involve a transmitting and/or receiving devices with a large numberof antennas (e.g., 12, 20, 64, 128, etc.), which may facilitate precisebeamforming or spatial diversity unattainable with devices operatingwith fewer antennas according to legacy protocols (e.g., WiFi or LTE).

The base station 910 and small cell 930 may wirelessly communicate withdevices in the system 900 or other communication-capable devices in thesystem 900 having at the least a sensor wireless network, such as solarcells 937 that may operate on an active/sleep cycle, and/or one or moreother sensor devices. The base station 910 may provide wirelesscommunications coverage for devices that enter its coverages area, suchas the mobile device 915 and the drone 917. The small cell 930 mayprovide wireless communications coverage for devices that enter itscoverage area, such as near the building that the small cell 930 ismounted upon, such as vehicle 945 and drone 917.

Generally, a small cell 930 may be referred to as a small cell andprovide coverage for a local geographic region, for example, coverage of200 meters or less in some examples. This may contrasted with atmacrocell, which may provide coverage over a wide or large area on theorder of several square miles or kilometers. In some examples, a smallcell 930 may be deployed (e.g., mounted on a building) within somecoverage areas of a base station 910 (e.g., a macrocell) where wirelesscommunications traffic may be dense according to a traffic analysis ofthat coverage area. For example, a small cell 930 may be deployed on thebuilding in FIG. 9 in the coverage area of the base station 910 if thebase station 910 generally receives and/or transmits a higher amount ofwireless communication transmissions than other coverage areas of thatbase station 910. Abase station 910 may be deployed in a geographic areato provide wireless coverage for portions of that geographic area. Aswireless communications traffic becomes more dense, additional basestations 910 may be deployed in certain areas, which may alter thecoverage area of an existing base station 910, or other support stationsmay be deployed, such as a small cell 930. Small cell 930 may be afemtocell, which may provide coverage for an area smaller than a smallcell (e.g., 100 meters or less in some examples (e.g., one story of abuilding)).

While base station 910 and small cell 930 may provide communicationcoverage for a portion of the geographical area surrounding theirrespective areas, both may change aspects of their coverage tofacilitate faster wireless connections for certain devices. For example,the small cell 930 may primarily provide coverage for devicessurrounding or in the building upon which the small cell 930 is mounted.However, the small cell 930 may also detect that a device has entered iscoverage area and adjust its coverage area to facilitate a fasterconnection to that device.

For example, a small cell 930 may support a massive MIMO connection withthe drone 917, which may also be referred to as an unmanned aerialvehicle (UAV), and, when the vehicle 945 enters it coverage area, thesmall cell 930 adjusts some antennas to point directionally in adirection of the vehicle 945, rather than the drone 917, to facilitate amassive MIMO connection with the vehicle, in addition to the drone 917.In adjusting some of the antennas, the small cell 930 may not support asfast as a connection to the drone 917 at a certain frequency, as it hadbefore the adjustment. For example, the small cell 930 may becommunicating with the drone 917 on a first frequency of variouspossible frequencies in a 4G LTE band of 1.8 GHz. However, the drone 917may also request a connection at a different frequency with anotherdevice (e.g., base station 910) in its coverage area that may facilitatea similar connection as described with reference to the small cell 930,or a different (e.g., faster, more reliable) connection with the basestation 910, for example, at a 3.5 GHz frequency in the 5G NR band. Insome examples, drone 917 may serve as a movable or aerial base station.Accordingly, the system 900 may enhance existing communication links incompensating, at least partially, (I/Q) imbalance for devices thatinclude DACs or mixers, for example, in both the 4GE LTE and 5G NRbands.

The wireless communications system 900 may include devices such as basestation 910, communication device 920, and small cell 930 that maysupport several connections at varying frequencies to devices in thesystem 900, while also at least partially compensating for (I/Q)imbalance utilizing recurrent neural networks, such as recurrent neuralnetwork 1180 or recurrent neural network 1294. Such devices may operatein a hierarchal mode or an ad-hoc mode with other devices in the networkof system 900. While described in the context of a base station 910,communication device 920, and small cell 930, it can be appreciated thatother devices that can support several connections with devices in thenetwork, while also at least partially compensating for (I/Q) imbalanceutilizing recurrent neural networks, may be included in system 900,including but not limited to: macrocells, femtocells, routers,satellites, and RFID detectors.

In various examples, the elements of wireless communication system 900,such as base station 910, a mobile device 915, a drone 917,communication device 920, a small cell 930, and vehicles 940, 945, maybe implemented as an electronic device described herein that at leastpartially compensate for (I/Q) imbalance utilizing recurrent neuralnetworks. For example, the communication device 920 may be implementedas electronic devices described herein, such as electronic device 102,110 of FIG. 1, electronic device 200 of FIG. 2, recurrent neural network512 of FIGS. 5C-5E, electronic device 800 of FIG. 8, electronic device1100 of FIG. 11, electronic device 1200 of FIG. 12, or any system orcombination of the systems depicted in the Figures described herein,such as in conjunction with a time frame 600 of FIG. 6.

FIG. 10 illustrates an example of a wireless communications system 1000in accordance with aspects of the present disclosure. The wirelesscommunications system 1000 includes a mobile device 1015, a drone 1017,a communication device 1020, and a small cell 1030. A building 1010 alsoincludes devices of the wireless communication system 1000 that may beconfigured to communicate with other elements in the building 1010 orthe small cell 1030. The building 1010 includes networked workstations1040, 1045, virtual reality device 1050, IoT devices 1055, 1060, andnetworked entertainment device 1065. In the depicted system 1000, IoTdevices 1055, 1060 may be a washer and dryer, respectively, forresidential use, being controlled by the virtual reality device 1050.Accordingly, while the user of the virtual reality device 1050 may be indifferent room of the building 1010, the user may control an operationof the IoT device 1055, such as configuring a washing machine setting.Virtual reality device 1050 may also control the networked entertainmentdevice 1065. For example, virtual reality device 1050 may broadcast avirtual game being played by a user of the virtual reality device 1050onto a display of the networked entertainment device 1065.

The small cell 1030 or any of the devices of building 1010 may beconnected to a network that provides access to the Internet andtraditional communication links. Like the system 900, the system 1000may facilitate a wide-range of wireless communications connections in a5G system that may include various frequency bands, including but notlimited to: a sub-6 GHz band (e.g., 700 MHz communication frequency),mid-range communication bands (e.g., 2.4 GHz), mmWave bands (e.g., 24GHz), or any other bands, such as a 1 MHz, 5 MHz, 10 MHz, 20 MHz band.Additionally or alternatively, the wireless communications connectionsmay support various modulation schemes as described above with referenceto system 900. System 1000 may operate and be configured to communicateanalogously to system 900. Accordingly, similarly numbered elements ofsystem 1000 and system 900 may be configured in an analogous way, suchas communication device 920 to communication device 1020, small cell 930to small cell 1030, etc.

Like the system 900, where elements of system 900 are configured to formindependent hierarchal or ad-hoc networks, communication device 1020 mayform a hierarchal network with small cell 1030 and mobile device 1015,while an additional ad-hoc network may be formed among the small cell1030 network that includes drone 1017 and some of the devices of thebuilding 1010, such as networked workstations 1040, 1045 and IoT devices1055, 1060.

Devices in communication system 1000 may also form (D2D) connectionswith other mobile devices or other elements of the system 1000. Forexample, the virtual reality device 1050 may form a narrowband IoTconnections with other devices, including IoT device 1055 and networkedentertainment device 1065. As described above, in some examples, D2Dconnections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by virtual reality device 1050.

In various examples, the elements of wireless communication system 1000,such as the mobile device 1015, the drone 1017, the communication device1020, and the small cell 1030, the networked workstations 1040, 1045,the virtual reality device 1050, the IoT devices 1055, 1060, and thenetworked entertainment device 1065, may be implemented as electronicdevices described herein that at least partially compensate for I/Qimbalance utilizing recurrent neural networks. For example, thecommunication device 1020 may be implemented as electronic devicesdescribed herein, such as electronic device 102, 110 of FIG. 1,electronic device 200 of FIG. 2, recurrent neural network 512 of FIGS.5C-5E, electronic device 800 of FIG. 8, electronic device 1100 of FIG.11, electronic device 1200 of FIG. 12, or any system or combination ofthe systems depicted in the Figures described herein, such as inconjunction with a time frame 600 of FIG. 6.

FIG. 13 illustrates an example of a wireless communication system 1300in accordance with aspects of the present disclosure. The wirelesscommunication system 1300 includes small cells 1310, camera devices1315, communication devices 1320, sensor devices 1330, communicationdevices 1340, data center 1350, and sensor device 1360. In the depictedsystem 1300, a small cell 1310 may form a hierarchal network, for anagricultural use, with a camera device 1315, communication devices 1320,sensor devices 1330, and sensor device 1360. Such a network formed bysmall cell 1310 may communicate data sets and inference results amongthe networked devices and the data center 1350. Continuing in thedepicted system 1300, another small cell 1310 may form anotherhierarchal network, for another agricultural use, with communicationdevices 1340, data center 1350, and sensor device 1360. Similarly, sucha network formed by the additional small cell 1310 may communicate datasets and inference results among the networked devices and the datacenter 1350. While depicted in certain agricultural networks withparticular small cells 1310, it can be appreciated that variousnetworks, whether hierarchal or ad-hoc, may be formed among the devices,cells, or data center of wireless communication system 1300.Additionally or alternatively, like the system 900 or system 1000, itcan be appreciated that similarly-named elements of system 1300 may beconfigured in an analogous way, such as communication device 920 tocommunication device 1320, communication device 1020 to communicationdevice 1340, or small cell 930 to small cell 1310, etc.

Like the system 1000, devices in system 1300 may also D2D connectionswith other mobile devices or other elements of the system 1000. Forexample, the communication device 1340 may form a narrowband IoTconnections with other devices, including sensor device 1360 orcommunication device 1320. As described above, in some examples, D2Dconnections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider,e.g., a cellular network or service provider of small cell 1310.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by the devices of system 1300.

In various examples, the elements of wireless communication system 1300,such as the camera device 1315, communication devices 1320, sensordevices 1330, communication devices 1340, sensor device 1360, may beimplemented as electronic devices described herein that compensate fornonlinear power amplifier noise utilizing recurrent neural networks. Forexample, the sensor device 1360 may be implemented as electronic devicesdescribed herein, such as electronic device 102, 110 of FIG. 1,electronic device 200 of FIG. 2, recurrent neural network 512 of FIGS.5C-5E, electronic device 800 of FIG. 8, or any system or combination ofthe systems depicted in the Figures described herein, such as inconjunction with a time frame 600 of FIG. 6. Accordingly, any of thedevices of system 1300 may transceive signals on both 4G and 5G bands;while also compensating for nonlinear power amplifier noise utilizingrecurrent neural networks.

The wireless communication system 1300 may also be implemented as a 5Gwireless communication system, having various mobile or other electronicdevice endpoints. As an example, the camera device 1315, including acamera (e.g., via an I/O interface 870) may be a mobile endpoint; andcommunication devices 1320 and sensor devices 1330 may be sensorendpoints in a 5G wireless communication system. Continuing in theexample, the camera device 1315 may collect data sets used for trainingand inference of machine learning or AI techniques at that respectivedevice, e.g., images of watermelons in an agricultural field. Such imagedata sets may be acquired by the camera device and stored in a memory ofthe camera device 1315 (e.g., such as memory units 850) to communicatethe data sets to the data center 1350 for training or processinginference results based on the image data sets. Using such data sets inan AI technique, the wireless communication system 1300 may determineinference results, such as a prediction of a growth rate of thewatermelons based on various stages of growth for different watermelonsin the agricultural field, e.g., a full-grown watermelon or anintermediate growth of a watermelon as indicated by a watermelon floweron the watermelon. For example, the data center 1350 may make inferenceresults using a recurrent neural network 840 on a cloud serverimplementing the electronic device 800, to provide such inferenceresults for use in the wireless communication system 1300.

Continuing in the example of the 5G communication system processinginference results with mobile and/or sensor endpoints, the communicationdevices 1320 may communicate data sets to the data center 1350 via thesmall cell 1310. For example, the communication devices 1320 may acquiredata sets about a parameter of the soil of the agricultural field inwhich the watermelons are growing via a sensor included on therespective communication device 1320 (e.g., a sensor 830). Suchparameterized data sets may also be stored at the communication device1320 or communicated to the data center 1350 for further training orprocessing of inference results using ML or AI techniques. While astatus of the agricultural field has been described with respect theexample of acquiring data sets about a parameter of the field, it can beappreciated that various data sets about a status of the agriculturalfield can be acquired, depending on sensors utilized by a communicationdevice 1320 to measure parameters of the agricultural field.

In some implementations, the communication device 1320 may makeinference results at the communication device 1320 itself. In suchimplementations, the communication device 1320 may acquire certainparameterized data sets regarding the soil and make inference resultsusing a recurrent neural network 840 on the communication device 1320itself. The inference result may be a recommendation regarding an amountof water for the soil of the agricultural field. Based on such aninference result, the communication device 1320 may be furtherconfigured to communicate that inference result to another device alongwith a control instruction for that device. Continuing in the example,the communication device 1320 may obtain such an inference result andgenerate an instruction for sensor device 1330 to increase or decreasean amount of water according to the inference result. Accordingly,inference results may be processed at devices of system 1300 or at thedata center 1350 based on data sets acquired by the various devices ofsystem 1300.

Continuing in the example of the 5G communication system processinginference results with mobile and/or sensor endpoints, the sensor device1330 may communicate data sets to the data center 1350 via the smallcell 1310. For example, the sensor device 1330 may acquire data setsabout water usage in the agricultural field in which the watermelons aregrowing because the sensor device is sprinkler implemented as anelectronic device as described herein, such as electronic device 800.For example, the sensor device 1330 may include a sensor 830 thatmeasures water usage, such as a water gauge. Accordingly, the sensordevice 1330 may acquire a data set regarding the water usage to bestored at memory units 850 or communicated to a data center 1350 forprocessing of inference results. Such parameterized data sets may alsobe stored at the communication device 1320 or communicated to the datacenter 1350 for further training or processing of inference resultsusing ML or AI techniques. While a status of water usage has beendescribed with respect to the example of a sprinkler and a water gauge,it can be appreciated that various data sets about a status of the waterusage can be acquired, depending on sensors utilized by a sensor device1320 to acquire data sets about water usage.

Additionally or alternatively in the example of the 5G communicationsystem processing inference results, the sensor device 1360 maycommunicate data sets or make inference results for use by one of theother devices of the system 1300 or the data center 1350. For example,the sensor device 1360 can acquire a dataset regarding a windspeed orother environmental condition of the agricultural setting of system1300. In the example, the sensor device 1360 may implement theelectronic device having a sensor 830 as an anemometer. Accordingly, inthe example the sensor 830 may acquire a windspeed and store a data setregarding that wind speed in one or more memory units 850. In someimplementations, the sensor device 1360 may utilize the recurrent neuralnetwork 840 to make inference results regarding the data set stored inthe memory units 850. For example, an inference result may be that acertain wind speed in a particular direction is indicative ofprecipitation. Accordingly, that inference results may be communicatedin a 5G transmission, including while other signals are communicated ona 4G band at the sensor device 1360 or small cell 1310, to the smallcell 1310.

In some implementations, the small cell 1310 may further route such aninference result to various devices or the data center 1350. Continuingin the example, the inference result from the sensor device 1360 may beutilized by the sensor devices 1330 to adjust a water usage in theagricultural field. In such a case, the sensor devices 1330 may processthe inference results from the sensor device 1330 at a recurrent neuralnetwork(s) 840 of the respective sensor devices 1330 to adjust the waterusage in the agricultural field growing watermelons based on theinference result that a certain wind speed in a particular direction isindicative of precipitation. Accordingly, advantageously, the system1300, in acquiring data sets and processing inference results atrespective recurrent neural networks 840, may provide a sustainabilityadvantage in conserving certain natural resources that the devices ofsystem 1300 may interact with, such as the sensor devices 1330interacting with a water natural resource. Using such inference results,the system 1300 can facilitate predictions about natural resourcesutilized by the agricultural field devices in system 1300.

As another example of the wireless communication systems 1300implementing a 5G wireless communication system, having various mobileor other electronic device endpoints, another camera device 1315,including a camera (e.g., via an I/O interface 870) and communicationdevices 1340 attached to certain agricultural livestock (e.g., cows asdepicted in FIG. 11) may be additional mobile endpoints; and sensordevice 1360 may be a sensor endpoint in a 5G wireless communicationsystem. In the example, the communication devices 1340 may beimplemented as certain narrowband IoT devices that may utilize lessenergy, resulting in power-efficiency and may utilize less complexsignaling frameworks, such that devices may transmit asynchronously onparticular bands. The I/O interface 870 of the communication devices maybe coupled to a Global Positioning System (GPS) device that provides alocation of the communication devices 1340 attached to certainagricultural livestock. The respective communication devices 1340 mayacquire respective location data sets that track the movement of thelivestock in an agricultural field. Advantageously, the communicationdevices 1340 may provide such data sets to other devices in the system1300, such as the data center 1350, for further processing of inferenceresults regarding the respective location data sets. Such data sets maybe communicated, from the respective communication devices 1340, in a 5Gtransmission; while other signals are communicated on a 4G band at thecommunication devices 1340 or small cell 1310, to the small cell 1310.

Continuing in the example, the camera device 1315 may also acquireimages of the livestock with the communication devices 1340 attachedthereto, for further processing of inference results with the respectivelocation datasets. In an example, the image data sets acquired by thecamera device 1315 and the location data sets acquired by thecommunication devices 1340 may be communicated to the data center 1350via the small cell 1310. Accordingly, the data center 1350 may make aninference result based on the image and location data sets. For example,a recurrent neural network 840 on a cloud server, implemented aselectronic device 800 at the data center 1350, may make an inferenceresult that predicts when the livestock are to be removed from theagricultural field due to a consumption of a natural resource. Theinference result may be based on the condition of the agricultural fieldbased on images from the image data set and the temporal location of thelivestock indicative of how long the livestock have consumed aparticular natural resource (e.g., grass) based on the location dataset. In some examples, the cloud server at the data center 1350 (oranother cloud server at the data center 1350) may further process thatinference result with an additional data set, such as a data setregarding the wind speed acquired by the sensor device 1360, to furtherprocess that inference result with an inference result regarding aprecipitation prediction based on a certain wind speed in a particulardirection. Accordingly, multiple inference results may be processed atthe data center 1350 based on various data sets that the devices ofsystem 1300 acquire.

Therefore, the system 1300 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 1300 when implementing such recurrent neural networks asdescribed herein that enable high-capacity training and inference, whilealso increasing precision of inference results, e.g., includinghigher-order memory effects in a recurrent neural network 512 of FIGS.5C-5E. For example, various devices of system 1300 may facilitateprocessing of data acquired, e.g., to efficiently offload such data todata center 1350 for AI or machine learning (ML) techniques to beapplied. Accordingly, the system 1300 may include devices that cancelI/Q imbalance or noise related thereto of one or more devices of thesystem 1300 that are transceiving on both the 4G or 5G bands, forexample, such that other devices that may be operating as 5G standalonetransceiver systems can communicate with such 4G/5G devices in anefficient and timely manner.

FIG. 14 illustrates an example of a communication system 1400 inaccordance with aspects of the present disclosure. The communicationsystem 1400 includes small cells 1410, wired communication link 1412,drone 1417, industrial user 1420, industrial communication device 1427,substation 1430, industrial pipeline 1425, pipeline receiving station1435, pipeline communication device 1437, residential user 1440,commercial user 1445, data center 1450, sensor device 1455, powergeneration user 1460, fuel station 1470, substation 1475, and fuelstorage 1480.

In the depicted communication system 1400, small cells 1410 may form ahierarchal network to provide a status of the fuel for various users ofthe industrial pipeline system, thereby facilitating fuel transmission,distribution, storage, or power generation based on distributed fuel.The fuel may be various types of gas or oil, for example, crude oil,diesel gas, hydrogen gas, or natural gas. The fuel may be provided andutilized by an industrial user 1420, substation 1430 or substation 1475,residential user 1440, commercial user 1445, or fuel station 1470.Various statuses regarding the fuel may be provided to small cells 1410,drone 1417, data center 1450, or wired communication link 1412 by thevarious communication devices in such an industrial communication system1400. For example, industrial communication device 1427, pipelinecommunication device 1437, or sensor device 1455 may provide a status asto a flow of the fuel through the pipeline network depicted in FIG. 14.Additionally or alternatively, the fuel may be provided through thepipeline network for use in power generation at power generation user1460 or for storage at fuel storage 1480. The fuel is provided to thepipeline network by industrial pipeline 1425 at pipeline receivingstation 1435.

As fuel flows through the pipeline network, industrial communicationdevice 1427, pipeline communication device 1437, or sensor device 1455may be implemented as electronic devices 800 with sensors 830 or I/Ointerfaces 870 coupled to various I/O devices to receive data input asto a status of the fuel. Accordingly, data sets regarding the fuel maybe acquired by the industrial communication device 1427, pipelinecommunication device 1437, or sensor device 1455 for further processingof inference results regarding a status of the fuel. For example, thepipeline communication device 1437 may communicate via a 5Gcommunications signal a status indicative of power consumption atvarious users of the pipeline network, such as industrial user 1420,residential user 1440, or commercial user 1445. As another example,substation 1430 or substation 1475 may provide a power generation statusas to power generated by elements of pipeline network coupled to thesubstations 1430 or substation 1475. Accordingly, substation 1430 mayprovide a power generation status of industrial user 1420; andsubstation 1475 may provide a power generation status as powergeneration user 1460. Such various statuses may be provided to the datacenter 1450 via drone 1417 or small cells 1410 communicating withdevices located at the respective users of the pipeline network ordevices located at the substations 1430 or 1475. In the implementationof system 1400, a fuel storage status may also be provided to the datacenter 1450 by the fuel storage 1480.

While system 1400 is depicted in a particular pipeline network system,it can be appreciated that various networks, whether hierarchal orad-hoc, may be formed among the devices, cells, or data center 1450 ofwireless communication system 1400. Additionally or alternatively, likethe system 900, system 1000, system 1100, it can be appreciated thatsimilarly-named elements of system 1400 may be configured in ananalogous way, such as communication device 920 to pipelinecommunication device 1437, drone 917 to drone 1417, or small cell 930 tosmall cell 1410, etc.

Like the system 1100, devices in system 1400 may also D2D connectionswith other mobile devices or other elements of the system 1400. Forexample, the pipeline communication device 1437 may form a narrowbandIoT connections with other devices, including industrial communicationdevice 1427 or sensor device 1455. As described above, in some examples,D2D connections may be made using licensed spectrum bands, and suchconnections may be managed by a cellular network or service provider,e.g., a cellular network or service provider of small cell 1410.Accordingly, while the above example was described in the context of anarrowband IoT, it can be appreciated that other device-to-deviceconnections may be utilized by the devices of system 1400.

In various examples, the elements of wireless communication system 1400,such as the industrial communication device 1427, pipeline communicationdevice 1437, or sensor device 1455, may be implemented as electronicdevices described herein that compensate for I/Q imbalance or noiserelated thereto utilizing recurrent neural networks. For example, thesensor device 1455 may be implemented as electronic devices describedherein, such as electronic device 102, 110 of FIG. 1, electronic device200 of FIG. 2, recurrent neural network 512 of FIGS. 5C-5E, electronicdevice 800 of FIG. 8, electronic device 1100 of FIG. 11, electronicdevice 1200 of FIG. 12, or any system or combination of the systemsdepicted in the Figures described herein, such as in conjunction with atime frame 600 of FIG. 6. Accordingly, any of the devices of system 1400may transceive signals on both 4G and 5G bands; while also compensatingfor I/Q imbalance or noise related thereto utilizing recurrent neuralnetworks.

In an example of processing industrially-acquired data sets of thesystem 1400, the devices of system 1400, such as industrialcommunication device 1427, pipeline communication device 1437, or sensordevice 1455, and users of system 1400 may communicate, via communicated5G signals, data sets regarding a status of the fuel, power consumption,or power generation, to the data center 1450 for further processing ofinference results. In an example, a fuel flow status at sensor device1455 may be communicated to the data center 1450 via the small cell1410. As another example, a power consumption status of residential user1440 may be communicated via a 5G communications signal to the datacenter 1450 via small cell 1410 or drone 1417. As yet another example, apower generation status may be communicated via a 5G communicationssignal to the small cell 1410 from power generation user 1460, and thenfurther communicated to the data center 1450 via a wired communicationlink 1412. Accordingly, the data center 1450 may acquire data sets fromvarious communication devices or users of system 1400. The data centermay process one or more inference results based on such acquired datasets. For example, a recurrent neural network 840 on a cloud server,implemented as electronic device 800 at the data center 1450, may makean inference result that predicts when a fuel shortage or surplus mayoccur based on a power consumption status at various users of the system1400 and a fuel flow status received from pipeline communication device1437 detecting a fuel flow from pipeline 1425 via pipeline receivingstation 1435. In some examples, the cloud server at the data center 1450(or another cloud server at the data center 1450) may further processthat inference result with one or more additional data sets, to furtherprocess that inference result with one or more other inference results.Accordingly, multiple inference results may be processed at the datacenter 1450 based on various data sets that the devices of system 1400acquire. Therefore, the devices of system 1400 may facilitate processingof data acquired, e.g., to efficiently offload such data to data center1450 for AI or machine learning (ML) techniques to be applied.

Accordingly, the system 1400 may facilitate the acquisition andcommunication of data sets or inference results for various devices inthe system 1400 when implementing such recurrent neural networks asdescribed herein that enable higher-capacity training and inference,while also increasing precision of inference results, e.g., includinghigher-order memory effects in a recurrent neural network 512 of FIGS.5C-5E. Accordingly, the system 1400 may include devices that cancel I/Qimbalance or noise related thereto of one or more devices of the system1400 that are transceiving on both the 4G or 5G bands, for example, suchthat other devices that may be operating as 5G standalone transceiversystems can communicate with such 4G/5G devices in an efficient andtimely manner.

Moreover, such 5G standalone systems may be preferable to operate in aremote setting, e.g., not near a city with a wireless metropolitanaccess network (MAN)). Such 5G systems can operate over greaterdistances (e.g., 1 km, 5 km, 50 km, 500 km, or 5000 km); in contrast toa metropolitan-geographic area, which may be restricted to smallerdistances (e.g., 10m, 100 m, 1 km, or 5 km). Accordingly, a 5Gtransceiver system may need to communicate long distances in anenvironment with various degrading environmental effects. Therefore, the5G systems and devices described herein can communicate data in wirelessenvironments that experience effects of weather conditions over greatdistances and/or other environmental effects to the wirelessenvironment.

On top of the challenges of environmental effects and distance, thetransceiver system may experience interference. For example, in contrastto a conventional wireless MAN system that may have a line-of-sight(LOS) with a wireless subscriber, a 5G wireless system may include adata center communicating with a remote agricultural device that isexperiencing cloudy weather in a temperate environment (e.g., the PugetSound region). As such, the remote agricultural device may not have adirect LOS with the data center because the LOS is occluded by clouds orother environmental factors. In such a case, examples described hereinmay compensate for I/Q imbalance or noise related thereto; as well ascompensating for the environmental effects that a 5G communicationsignal may experience due to its communication path over a greaterdistance than that of a wireless MAN.

In compensating or reducing I/Q imbalance or noise related thereto usingRNNs, wireless systems and devices described herein may increasecapacity of their respective communication networks, with such systemsbeing more invariant to noise than traditional wireless systems that donot use RNNs (e.g., utilizing time-delayed versions of processingresults). For example, the recurrent neural networks may be used toreduce I/Q imbalance or noise related thereto that will be present intransmitted signals (e.g., processed signals from a DAC or RF mixer)based partly on the signals to be transmitted, e.g., in filteringtransmission signals at a digital filter (e.g., DPM filter 1118), priorto processing transmission signals at a DAC 1130 or mixer 1137, or inthe example of FIG. 12, processing transmission signals at D/Aconverters 1230 or mixer 1237. For example, using time-delayed versionsof processing results in an RNNs, the I/Q imbalance processed by suchcomponents may be compensated or reduced, as the RNN utilizes feedbacksignals X(n) 1177 or feedback signals X(n) 1249 with respect to thetime-delayed versions of the input data (e.g., power amplifier outputdata), to generate filter coefficient data as output data from the RNN.In this manner, recurrent neural networks may be used to reduce and/orimprove errors which may be introduced by such I/Q imbalance or noiserelated thereto. Advantageously, with such an implementation, wirelesssystems and devices implementing such RNNs increase capacity of theirrespective wireless networks because additional data may be transmittedin such networks, which would not otherwise be transmitted due to theeffects of I/Q imbalance, e.g., which limits the amount of data to betransmitted due to compensation schemes in traditional wireless systems.

As an additional or alternative advantage, a DAC, in a standalone 5Gtransceiver that may be preferable to operate in a remote setting, mayconvert wireless transmission signals at higher conversion rates (e.g.,to operate over greater distances of 5 km, 50 km, 500 km, or 5000 km).As another additional or another advantage, a mixer, in a standalone 5Gtransceiver that may be preferable to operate in a remote setting, maymix wireless transmission signals at higher mixing rates (e.g., tooperate over greater distances of 5 km, 50 km, 500 km, or 5000 km).Accordingly, a 5G transceiver system may need to communicate longdistances in an environment with various degrading environmentaleffects. The 5G systems and devices described herein can communicatedata in wireless environments that experience effects of weatherconditions over great distances and/or other environmental effects tothe wireless environment, while also compensating for such I/Q imbalanceby providing filter coefficient data from a recurrent neural network toa DPM filter. For example, the recurrent neural network 1180 orrecurrent neural network 1294 may calculate or generate filtercoefficient data as a dataset, which would be difficult to model,without the utilization of the RNNs described herein. In someimplementations, large datasets of filter coefficient data may beutilized by the DPD filter to compensate such I/Q imbalance or noiserelated thereto, while also transmitting in an environment withdegrading environmental effects. Accordingly, a wireless device orsystem that implements a NN or RNN, as described herein, may calculatefilter coefficient data representative of U/Q imbalance, such as thoseinvolved in processing wireless input data having time-varying wirelesschannels (e.g., autonomous vehicular networks, drone networks, orInternet-of-Things (IoT) networks).

Certain details are set forth above to provide a sufficientunderstanding of described examples. However, it will be clear to oneskilled in the art that examples may be practiced without various ofthese particular details. The description herein, in connection with theappended drawings, describes example configurations and does notrepresent all the examples that may be implemented or that are withinthe scope of the claims. The terms “exemplary” and “example” as may beused herein means “serving as an example, instance, or illustration,”and not “preferred” or “advantageous over other examples.” The detaileddescription includes specific details for the purpose of providing anunderstanding of the described techniques. These techniques, however,may be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form inorder to avoid obscuring the concepts of the described examples.

Information and signals described herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the above description may berepresented by voltages, currents, electromagnetic waves, magneticfields or particles, optical fields or particles, or any combinationthereof.

Techniques described herein may be used for various wirelesscommunications systems, which may include multiple access cellularcommunication systems, and which may employ code division multipleaccess (CDMA), time division multiple access (TDMA), frequency divisionmultiple access (FDMA), orthogonal frequency division multiple access(OFDMA), or single carrier frequency division multiple access (SC-FDMA),or any a combination of such techniques. Some of these techniques havebeen adopted in or relate to standardized wireless communicationprotocols by organizations such as Third Generation Partnership Project(3GPP), Third Generation Partnership Project 2 (3GPP2) and IEEE. Thesewireless standards include Ultra Mobile Broadband (UMB), UniversalMobile Telecommunications System (UMTS), Long Term Evolution (LTE),LTE-Advanced (LTE-A), LTE-A Pro, New Radio (NR), IEEE 802.11 (WiFi), andIEEE 802.16 (WiMAX), among others.

The terms “5G” or “5G communications system” may refer to systems thatoperate according to standardized protocols developed or discussedafter, for example, LTE Releases 13 or 14 or WiMAX 802.16e-2005 by theirrespective sponsoring organizations. The features described herein maybe employed in systems configured according to other generations ofwireless communication systems, including those configured according tothe standards described above.

The various illustrative blocks and modules described in connection withthe disclosure herein may be implemented or performed with ageneral-purpose processor, a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices (e.g., a combinationof a DSP and a microprocessor, multiple microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration).

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes bothnon-transitory computer storage media and communication media includingany medium that facilitates transfer of a computer program from oneplace to another. A non-transitory storage medium may be any availablemedium that can be accessed by a general purpose or special purposecomputer. By way of example, and not limitation, non-transitorycomputer-readable media can comprise RAM, ROM, electrically erasableprogrammable read only memory (EEPROM), or optical disk storage,magnetic disk storage or other magnetic storage devices, or any othernon-transitory medium that can be used to carry or store desired programcode means in the form of instructions or data structures and that canbe accessed by a general-purpose or special-purpose computer, or ageneral-purpose or special-purpose processor.

Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium.Combinations of the above are also included within the scope ofcomputer-readable media.

Other examples and implementations are within the scope of thedisclosure and appended claims. For example, due to the nature ofsoftware, functions described above can be implemented using softwareexecuted by a processor, hardware, firmware, hardwiring, or combinationsof any of these. Features implementing functions may also be physicallylocated at various positions, including being distributed such thatportions of functions are implemented at different physical locations.

Also, as used herein, including in the claims, “or” as used in a list ofitems (for example, a list of items prefaced by a phrase such as “atleast one of” or “one or more of”) indicates an inclusive list suchthat, for example, a list of at least one of A, B, or C means A or B orC or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein,the phrase “based on” shall not be construed as a reference to a closedset of conditions. For example, an exemplary step that is described as“based on condition A” may be based on both a condition A and acondition B without departing from the scope of the present disclosure.In other words, as used herein, the phrase “based on” shall be construedin the same manner as the phrase “based at least in part on.”

From the foregoing it will be appreciated that, although specificexamples have been described herein for purposes of illustration,various modifications may be made while remaining with the scope of theclaimed technology. The description herein is provided to enable aperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the scope of thedisclosure. Thus, the disclosure is not limited to the examples anddesigns described herein, but is to be accorded the broadest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. An apparatus, comprising: a transceiverconfigured to generate, using a digital-to-analog converter (DAC), aplurality of radio frequency (RF) signals for transmission; a pluralityof multiplication/accumulation units (MAC units), including: first MACunits of the plurality of MAC units configured to mix the plurality ofRF signals received via a receive path of the transceiver as feedbackand signaling that is based on respective outputs of the first MAC unitsusing a plurality of coefficients to generate first intermediateprocessing results; and additional MAC units of the plurality of MACunits, the additional MAC units configured to mix the first intermediateprocessing results and signaling that is based on respective outputs ofthe respective additional MAC units using additional coefficients of theplurality of coefficients to generate filter coefficient data; and adigital filter configured to filter signals prior to an input to the DACin accordance with the filter coefficient data calculated using theplurality of MAC units.
 2. The apparatus of claim 1, wherein the digitalfilter is further configured to provide output data as a compensatedplurality of RF signals, the output data based partly on the filtercoefficient data.
 3. The apparatus of claim 1, wherein a recurrentneural network (RNN) is configured to provide the filter coefficientdata to the digital filter.
 4. The apparatus of claim 3, wherein the RNNis configured receive the plurality of RF signals as feedback via thereceive path of the transceiver.
 5. The apparatus of claim 1, furthercomprising: a switch coupled to the transceiver and configured to:activate a switch path that provides the plurality of RF signals as thefeedback from a transmit path of the transceiver via the receive path ofthe transceiver.
 6. The apparatus of claim 5, wherein the switch isfurther configured to: receive a selection signal indicating whether theswitch path is to be activated, the selection signal based at least inpart on a transmission time interval (TTI) of a time-division duplex(TDD) configured radio frame
 7. The apparatus of claim 6, wherein theselection signal indicating the switch path is to be activated is basedat least in part on an uplink TTI of the TDD configured radio frame. 8.The apparatus of claim 6, wherein the selection signal indicating theswitch path is to be deactivated is based at least in part on a downlinkTTI of the TDD configured radio frame.
 9. The apparatus of claim 6,wherein the switch is further configured to, responsive to the selectionsignal indicating the switch path is to be activated, activate theswitch path that provides feedback via the receive path of thetransceiver, and wherein the receive path of the transceiver is coupledto the transmit path of the transceiver when the switch path isactivated.
 10. The apparatus of claim 6, wherein the switch is furtherconfigured to deactivate the switch path responsive to the selectionsignal indicating the switch path is to be deactivated.
 11. Theapparatus of claim 1, wherein the transceiver is further configured togenerate the plurality of radio frequency (RF) signals for transmissionusing a RF mixer, wherein the digital filter is further configured tofilter signals prior to the input to the DAC and prior to an input tothe RF mixer, in accordance with the filter coefficient data calculatedusing the plurality of MAC units
 12. The apparatus of claim 1, whereinthe plurality of RF signals include data indicative of at least one of alivestock status, a water usage status, an agricultural field status, awind measurement, a power generation status, an oil flow status, anenergy storage status, or a power consumption status.
 13. An apparatus,comprising: a plurality of transceivers each configured to provide arespective signal of a plurality of signals to a respective antenna of aplurality of antennas; a plurality of digital-to-analog converters(DACs) configured to receive respective signals and to convert therespective signals to generate respective converted signals; a processorcomprising a recurrent neural network (RNN) configured to generatefilter coefficient data according to the respective converted signals;and a filter configured to filter signals prior to a respective input ofa DAC of the plurality of DACs in accordance with the filter coefficientdata.
 14. The apparatus of claim 13, wherein the RNN comprising: aplurality of processing units configured for bit manipulation andconfigured to receive the respective amplified signals; and a pluralityof multiplication/accumulation (MAC) units, each MAC unit configured togenerate a plurality of noise processing results based on the respectiveconverted signal and a delayed version of at least one of the pluralityof noise processing results.
 15. The apparatus of claim 14, wherein theRNN further comprising: a plurality of memory look-up units (MLUs)configured to store and provide respective noise processing results,wherein a portion of the plurality of the MLUs configured to provideoutput data based on the respective converted signals being mixed usinga plurality of coefficients.
 16. The apparatus of claim 15, furthercomprising: a plurality of delay units, each delay unit associated witha respective MAC unit and configured to provide the delayed versions ofrespective outputs of the plurality of MAC units based on a portion ofthe respective noise processing results provided by respective MLUs ofthe plurality of MLUs.
 17. The apparatus of claim 13, furthercomprising: a plurality of switches, each switch configured to: activatea respective switch path that provides the respective amplified signalfrom a respective transmit path of a respective transceiver to the RNNvia a receive path of that transceiver or via a respective receive pathof a respective transceiver of the plurality of transceivers.
 18. Theapparatus of claim 13, wherein the apparatus implements anInternet-of-Things (IoT) smart home device, an IoT agricultural deviceor an IoT industrial device.
 19. A method, comprising: generating, in atransmit path of a transceiver, a first transmission signal based partlyon a first input signal to be transmitted, wherein generating thetransmission signal comprises converting the first transmission signalusing a digital-to-analog converter (DAC); calculating, at a recurrentneural network (RNN) coupled to the transmit path via a switch path,first processing results based on the first transmission signal as inputdata and delayed versions of respective outputs of a first plurality ofmultiplication/accumulation processing units (MAC units) with aplurality of coefficients; calculating, at the RNN, output data based onthe first processing results and delayed versions of respective outputsof a respective additional plurality of MAC units with an additionalplurality of coefficients; and filtering a second transmission signal inaccordance with the output data.
 20. The method of claim 19, furthercomprising: providing, after activating the switch path, the firsttransmission signal as feedback from the transmit path of a transceiverto the RNN as the input data via a receive path of the transceiver. 21.The method of claim 20, further comprising: activating the switch pathbased at least in part on receiving a selection signal; and receivingthe selection signal that indicates the switch path is to be activatedwhen a transmission time interval (TTI) of a time-division duplex (TDD)configured radio frame is designated for an uplink transmission.
 22. Themethod of claim 21, further comprising, when the switch path isactivated, coupling the receive path of the transceiver to the transmitpath of the transceiver.
 23. The method of claim 19, further comprising:providing, from the RNN, the output data as filter coefficient data to adigital filter that at least partially compensates I/Q imbalance intransmission signals.
 24. The method of claim 19, wherein generating thetransmission signal further comprises mixing the first transmissionsignal using a radio frequency (RF) mixer, and wherein filtering thesecond transmission signal in accordance with the output data furthercomprises filtering the second transmission signal in accordance withthe output data prior to mixing the second transmission signal using theRF mixer.
 25. The method of claim 19, wherein filtering the secondtransmission signal in accordance with the output data comprisesfiltering the second transmission signal in accordance with the outputdata, prior to converting the second transmission signal using the DAC.